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The Productive Value Bridge Between Behavioral and Traditional Economics

Abstract Information

Societies consist of individuals who must interact with each other to survive. These interactions exchange something we define as Productive Value (PV). PV is defined as information, services, goods, currencies and other types of quantifiable value, aligning with concepts from information economics (Stiglitz, 2000). The ability to generate PV comes from an individual's Productive Power (PP), which parallels human capital theory (Becker, 1964). PP comes from an individual's knowledge and physical abilities. An individual's knowledge is a function of past interactions, or experiences, reflecting principles from behavioral economics (Kahneman & Tversky, 1979). Exchanges involving physical work derive from our ability to convert potential muscle energy to kinetic energy.

Individuals both profit and lose in interactions, thereby increasing or decreasing their PP, a concept that aligns with utility theory (Von Neumann & Morgenstern, 1944). Those individuals who profit more than others over time gain a competitive advantage because of increased PP, reminiscent of the resource-based view in strategic management (Barney, 1991).

The Productive Value Model for Bridging Behavioral and Traditional Economics looks at the way these interactions occur. It proposes a simple mathematical relationship for the PV exchanges. In addition to examining the traditional categories of value (currencies, goods, or services), the model introduces information as a new category of value. Furthermore, the utility gained in any value exchanges are examined for profit or loss, with the resulting change to an individual's Productive Power (including knowledge, skills, and well-being). This approach integrates aspects of endogenous growth theory (Romer, 1994) and evolutionary economics (Nelson & Winter, 1982).

Table of Contents

The Productive Value Bridge Between Behavioral and Traditional Economics

Abstract

Postulates

Productive Value and Productive Power

Information Both is and Describes Productive Value

Categories of Productive Power

Measuring Productive Value Exchanges

Willing Interactions versus Unwilling Interactions

Profit versus Loss and Risk

Evaluating Productive Power

Magnifying Productive Value

Technology

Teamwork

Natural Selection and Survival of the Fittest

Natural Selection

The Productive Power of Societies

The Importance of Societal Defense

Why Some Demographics Fall Economically Behind Others

The Bridge to Behavioral Economics

The Bridge to Traditional Economics

Summary

Additional Topics for Examination

Applicability to Species other than Humans

On Well Being and Mental Health

Investigating and Quantifying Non-typical Productive Power Group Categories

Examining the Productive Power of Past Competing Societies

Further Mathematical Investigation

Further Analysis of Information within the Model

Separated Parent Families

Analysis of Militaries

Appendix A – Case Study – Ukraine Russia Conflict Military Losses

References

Postulates Information

Traditional economics is the study of the marketplace, while behavioral economics attempts to describe "why" individuals in the marketplace do what they do. This paper presents a model to bridge these two fields of study, building upon existing economic frameworks.

The model makes two fundamental assumptions:

1.     Individuals require other individuals and interact to survive. This aligns with the concept of social capital (Coleman, 1988), which emphasizes the importance of social relationships in economic outcomes.

2.     Individuals exchange something of value with others when they interact. This postulate is consistent with the basic premises of exchange theory in economics (Homans, 1958) and relates to utility theory (Von Neumann & Morgenstern, 1944).

These postulates form the foundation for our Productive Value (PV) and Productive Power (PP) concepts. PV encompasses all forms of exchangeable value, including information, services, goods, and currencies. This broad definition of value aligns with information economics (Stiglitz, 2000) and extends beyond traditional economic measures.

PP, defined as the ability to generate PV, is analogous to human capital (Becker, 1964) but encompasses a wider range of capabilities. It includes not only education and skills but also the ability to process information and make decisions, reflecting insights from behavioral economics (Kahneman & Tversky, 1979).

By integrating these concepts, our model aims to provide a more comprehensive framework for understanding economic interactions, bridging the gap between traditional market analysis and individual decision-making processes.

Productive Value and Productive Power Information

Individuals exchange value with others through various forms of interaction. This exchange process is fundamental to economic activity and social interaction (Homans, 1958). We define all things exchanged between individuals as Productive Value, or PV. This concept of PV extends beyond traditional economic measures to include not only money, goods, and services, but also information. This broader definition aligns with information economics (Stiglitz, 2000), recognizing the crucial role of information in economic exchanges.

We define the ability to generate PV to exchange as Productive Power, or PP. This concept of PP has similarities to human capital theory (Becker, 1964), which views individuals' skills, knowledge, and experiences as assets. However, PP encompasses a wider range of capabilities, including the potential energy stored in our muscles and the knowledge in our brains.

To understand our model, let's start with an analogy:

Think about electricity for a minute. Imagine a wire connected to two sources, each able to send electrical power to the other. Each has a battery containing stored power as well as a mechanism to send or receive power. Now consider a case where the energies exchanged are usually in pairs and related: When one battery sends power to the other, most often the other battery reciprocates and sends power to the first battery as well. Furthermore, the power exchanged can be positive or negative; each side can not only send power to the other but "steal" it as well.

This analogy illustrates the dynamic nature of PV exchanges in human interactions, reflecting aspects of game theory (Von Neumann & Morgenstern, 1944) and social exchange theory (Emerson, 1976).

Our "battery" is the knowledge in our brain, the potential power stored in our muscles, as well as the goods we store and the services we are capable of performing. With them, we exchange PV in "Interactions". Interactions are defined simply as any exchange of PV between two individuals.

For example, we exchange $1 in PV for one cup of coffee of PV. We pay our hair stylist PV in the form of dollars while the other individual exchanges PV in the form of services. Someone gives another a hot stock tip that turns a profit, and thus PV is received as a result. These examples illustrate how our model can encompass both traditional economic exchanges and information-based interactions, bridging concepts from traditional and behavioral economics (Kahneman & Tversky, 1979).

Opinions, a category of information, can sometimes add large increases to our PP. While opinions may contain the greatest amounts of PV exchanged in any economy, it is the day-to-day goods and services exchanges in the economy that society currently focuses on. GDP is the easiest to quantify if currencies are involved. In fact, we can measure all categories of PV in units of dollars if we so choose. Services are currently measured that way. Harder to measure are information exchanges that don't immediately have an obvious PV (although $300 for an hour's worth of information from an attorney and such is already included in current marketplace measurements). But by far, the majority of information exchanges of PV are not measured.

This challenge of measuring intangible value aligns with ongoing debates in economics about the limitations of GDP and the need for more comprehensive measures of economic well-being (Stiglitz, Sen, & Fitoussi, 2009).

To summarize, individuals exchange Productive Value (PV) in Interactions. Any positive PV received increases an individual's Productive Power (PP) while any negative PV received reduces PP. Examples of PV include (in historic order) information, services, goods, and eventually currencies. Examples of PP would include the potential power stored in our muscles, as well as the knowledge in our brains, and of course the currency and goods in our possession. This framework provides a more holistic view of economic interactions, integrating aspects of traditional economics, behavioral economics, and information economics.

Information Both is and Describes Productive Value

Individuals only voluntarily enter into interactions when an interaction is expected to yield a profit. This expectation aligns with the concept of rational choice theory in economics (Becker, 1976), though it's important to note that our model allows for a broader interpretation of "profit" beyond mere monetary gain.

How do we know whether an interaction is going to be profitable or not? Based on the information provided to us. Before individuals exchange PV, they use information to describe the PV they are about to offer in an exchange. This role of information in facilitating economic transactions is a cornerstone of information economics (Stiglitz, 2000).

One side shows a dollar bill and the other shows a cup of coffee. Perhaps the coffee is described as hot. Or one side explains (promotes) a good or service (like an excellent haircut) to the other to entice an exchange of PV. Perhaps, the store clerk describes the varied uses of a particular hand tool an individual is considering for purchase. These examples illustrate the concept of information asymmetry in markets (Akerlof, 1970), where one party has more or better information than the other.

There is one more element to understand. While information can describe PV, it also can be PV. A hot stock tip may have great value in and of itself, if the appropriate interactions are entered into such that an individual can convert that stock tip into a stock purchase and sale at a profit. This dual nature of information as both a facilitator of transactions and a valuable commodity itself is explored in the economics of information goods (Shapiro & Varian, 1998).

So, information both is and describes PV. Obviously, the quality of the information exchanged between two individuals is of utmost importance (what if the store clerk is lying?). Sometimes certifications are given to certify individuals who transact in exchanging information as PV (an attorney passes the bar exam certifying that the information he/she provides is of positive PV). In other cases, bad advice is given. These issues of information quality and credibility relate to the concept of signaling in economics (Spence, 1973) and the challenges of maintaining trust in economic relationships (Arrow, 1972).

This model is an attempt to quantify "all" of the PV we exchange, not just those involving direct use of currencies. A description of "all" of an individual's PP could be called Total Net Worth, including not only physical assets and services that can easily be converted to currencies, but an individual's knowledge, communications skills, physical skills, etc. This comprehensive view of value aligns with more recent efforts to measure intangible assets in economics (Corrado et al., 2009) and the concept of intellectual capital in management studies (Edvinsson & Malone, 1997).

Categories of Productive Power

Many of the PP categories are already tracked in our economy. Currency transactions sum to GDP, for example. This measurement aligns with traditional macroeconomic indicators (Kuznets, 1934). Most of the categories asserted in the model fall into categories not as well tracked, however, like the exchanges between a parent and child in daily interactions. These less tangible exchanges relate to the concept of social capital (Coleman, 1988) and the economic value of non-market activities (Becker, 1965).

Many of our services use a simple time and knowledge/skill measurement. A haircut is worth $20 and takes 20 minutes. An attorney charges $500 an hour for consultation. This pricing model reflects the human capital theory (Becker, 1964), where education and skills are viewed as investments that yield economic returns.

Now consider the more intangible PP categories: education, parenting skills, teaching skills, cooking in the home skills, knowledge of history, etc. The list might very well be endless. Nevertheless, we should attempt to quantify more of these attributes. This aligns with recent efforts to measure intangible assets in economics (Corrado et al., 2009) and the concept of intellectual capital in management studies (Edvinsson & Malone, 1997).

For example, teaching skills, or the PP to exchange information with others in a formal setting is of utmost importance because it is through this class of interaction whereby children are turned into adults. Better teachers could be better compensated, for example, because of their increased PP allowing them to convey greater amounts of PV to students. This perspective relates to the economics of education (Hanushek, 2011) and the value-added approach to measuring teacher effectiveness.

Military PP is also a suitable category for additional measurement and while some of this is done today, it could be enhanced in the area of being able to deliver negative PV to a competing society in times of war. Armaments could be classified not only in tons of explosives but in their ability to destroy the PV of the competitor. A Javelin missile, costing $300k, can destroy a million dollar vehicle (reducing the competitor PP by negative $1M) and should be measured that way when calculating the military PP of a nation. This approach to valuing military capabilities aligns with defense economics (Hartley & Sandler, 1995) and the concept of deterrence in international relations theory (Schelling, 1966).

Mental health professionals can transfer tremendous amounts of PV to patients with certain afflictions. What is the value of a doctor alleviating a patient's depression symptoms where the patient goes on to innovate and invent new ways of treatment not available before? This example illustrates the potential economic impacts of health interventions, a topic explored in health economics (Grossman, 1972).

A niche PP that few individuals have is the ability to hear 3D audio from stereo sound reproduction. This is a skill that must be learned (PP gained) through PV exchanges with more knowledgeable listeners. Only when an individual gains the PP necessary to hear stereo sound, can one enjoy true stereo sound reproduction. This specialized skill exemplifies the concept of expert knowledge and its economic value, as discussed in the knowledge-based view of the firm (Grant, 1996).

Measuring Productive Value Exchanges Information

The following equations are offered to represent a gain or loss in any interaction between two individuals:

NewPP1 = ExistingPP1 + PP1(PV2) - PV1 NewPP2 = ExistingPP2 + PP2(PV1) - PV2

where PPi(PVj) represent the real PPi gained from the exchange. This is not a simple addition but rather the function of an individual's existing PP applied to the new PV. It is here where technology plays a magnifying role.

This mathematical representation of productive value exchanges aligns with utility theory in economics (Von Neumann & Morgenstern, 1944), but extends it by considering the dynamic nature of an individual's capacity to generate and utilize value. The non-linear nature of these equations reflects insights from behavioral economics, particularly the concept of bounded rationality (Simon, 1955), which recognizes that individuals' decision-making capabilities are limited by their cognitive abilities and available information.

Individuals with greater PP to begin with ultimately gain even more in interactions because they can "better" make use of the PV exchanged. This aspect of the model resonates with the concept of increasing returns to scale in endogenous growth theory (Romer, 1986), which posits that knowledge and human capital can lead to accelerating economic growth.

Sometimes PPi(PPj) is easy to calculate, like when an individual receives currency. Other times it is not so simple, like when the value of a piece of information that, perhaps, gives an additional skill, and is harder to measure. Nonetheless, it is quantifiable. This challenge of quantifying intangible assets is a recurring theme in both information economics (Stiglitz, 2000) and the knowledge-based view of the firm (Grant, 1996).

Consider a few examples:

An individual with $5 exchanges $1 for a cup of coffee while the other individual, with a store of 5 cups of coffee, reduces his coffee store by one cup.

NewPP1 = is reduced to $4 in currency and adds $1 worth of coffee NewPP2 = is reduced to 4 cups of coffee but has increased in currency to $1

But it's not quite that simple. Each side can have a profit or loss in the exchange. Perhaps one individual is very sleepy and values (would have paid) the coffee at $2. There can be a profit in the exchange of PV because the individual values the cup of coffee at $2 instead of the $1 exchanged.

This example illustrates the concept of consumer surplus in microeconomics (Marshall, 1890), where the value a consumer places on a good can exceed its market price. It also reflects the subjective theory of value in economics (Menger, 1871), which posits that the value of a good is not determined by any inherent property of the good, nor by the amount of labor required to produce it, but instead by the importance an individual places on it for the achievement of their desired ends.

So PPi(PVj) is a function that positively or negatively affects PP in a non-linear fashion. Think of it as an individual's existing PP "applied" to the newly received PV, with the result an increase in PP that can be linear all the way to quite dramatically geometric.

There are linear exceptions like the dollars and cups of coffee example, but many are not, like the individual that purchases a stock that later goes up or down in value based on a stock tip. Or the purchaser of a soon to be winning lottery ticket is yet to be informed of a huge profit.

These non-linear outcomes align with prospect theory in behavioral economics (Kahneman & Tversky, 1979), which describes how people choose between probabilistic alternatives that involve risk, where the probabilities of outcomes are uncertain.

It should be noted that PP can decay. Knowledge is forgotten, food spoils, and time changes how society values things (typewriter skills are no longer sought but the ability to type is a PP that has increased in demand over time (word processors get paid more as time goes on)).

This concept of PP decay and evolving value of skills reflects the economic theory of creative destruction (Schumpeter, 1942), which describes the process of industrial mutation that continuously revolutionizes the economic structure from within, incessantly destroying the old one, incessantly creating a new one.

Many of these profits and losses are measured today, and many are not. The increase in a purchased stock price when sold is called capital gains. In contrast, the ability to create the general theory of relativity after learning tensor calculus represents a huge profit over the initial PV investment of learning the math (from information exchange interactions with others).

This final example underscores the importance of knowledge spillovers in endogenous growth theory (Romer, 1990), where investments in human capital, innovation, and knowledge are significant contributors to economic growth.

Willing Interactions versus Unwilling Interactions

So far, we have discussed interactions that yield either a profit or loss for each individual and that both individuals willingly take part in the interaction. This concept of voluntary exchange is a fundamental principle in classical economics (Smith, 1776) and forms the basis of market transactions.

However, some interactions are forced upon individuals. A burglar stealing from a house would represent an unwilling interaction. The house owner loses PP while the burglar walks away with the goods. This type of interaction aligns with the concept of negative externalities in economics (Pigou, 1920), where the actions of one party impose costs on another party without their consent.

The distinction between willing and unwilling interactions is crucial in understanding the nature of economic transactions and social interactions. Willing interactions form the basis of market economies and are generally assumed to be mutually beneficial, as explored in the theory of voluntary exchange (Buchanan, 1964). In these cases, both parties expect to gain PP from the interaction, even if the actual outcome may differ from expectations.

Unwilling interactions, on the other hand, often result in a unilateral transfer of PP from one party to another without compensation. These can take various forms beyond outright theft, including:

Coercion: Where one party is forced into an interaction under threat or duress.

Fraud: Where one party is deceived into an interaction based on false information.

Externalities: Where the actions of one party inadvertently affect the PP of another party not directly involved in the transaction.

The presence of unwilling interactions in an economy can lead to market failures and inefficiencies, as discussed in welfare economics (Arrow, 1951). They also raise important questions about property rights and the role of institutions in protecting individuals from unwanted PP transfers (North, 1990).

Understanding the difference between willing and unwilling interactions is essential for policymakers and economists. It informs discussions on topics such as:

The need for legal frameworks to protect property rights and enforce contracts (Coase, 1960).

The design of mechanisms to internalize externalities, such as Pigouvian taxes (Pigou, 1920).

The importance of information symmetry and transparency in markets to prevent fraud (Akerlof, 1970).

By incorporating both willing and unwilling interactions, our model of Productive Value and Productive Power provides a more comprehensive framework for understanding economic exchanges and their impacts on individual and societal well-being.

Profit versus Loss and Risk

Certainly! I'll rewrite the section "Profit versus Loss and Risk," incorporating relevant academic references while maintaining the key ideas of your original text:

Profit versus Loss and Risk

How do we know whether an interaction is going to be profitable or not? Based on the information available. Information is the key element in interactions. This fundamental role of information in decision-making aligns with the concept of information economics (Stiglitz, 2000) and the theory of rational expectations (Muth, 1961).

However, the information we receive may or may not turn out to be correct, or the PV received may suddenly decrease because some missing piece of information (you bought a car that turned out to be a lemon). This uncertainty in economic transactions is a central theme in the economics of information asymmetry (Akerlof, 1970) and the principal-agent problem (Jensen & Meckling, 1976).

We assess risk when we evaluate the information received from the other party to assess the quality of the PV being received (perhaps we think gold will go up in value after we acquire it). This process of risk assessment in economic decision-making is fundamental to expected utility theory (von Neumann & Morgenstern, 1944) and prospect theory (Kahneman & Tversky, 1979).

The concept of profit and loss in our model extends beyond mere financial outcomes. It encompasses all forms of PV, including intangible benefits and costs. This broader view of value aligns with recent efforts to measure well-being and quality of life beyond traditional economic indicators (Stiglitz, Sen, & Fitoussi, 2009).

Risk plays a crucial role in determining the potential profitability of an interaction. Individuals must weigh the potential gains against the possibility of losses, often with incomplete information. This decision-making process under uncertainty is a key focus of behavioral economics (Thaler, 2015).

Several factors influence the assessment of profit, loss, and risk in interactions:

1.     Information quality: The accuracy and completeness of available information affect the ability to predict outcomes (Arrow, 1996).

2.     Individual risk preferences: Some individuals are more risk-averse than others, affecting their willingness to engage in potentially profitable but uncertain interactions (Pratt, 1964).

3.     Past experiences: Previous interactions shape expectations and risk perceptions for future exchanges (Kahneman & Tversky, 1973).

4.     Time horizon: The expected duration over which profits or losses may materialize can influence decision-making (Loewenstein & Prelec, 1992).

1.     Social and cultural factors: Norms and values can affect how individuals perceive and evaluate potential profits and losses (Henrich et al., 2001).

Understanding the interplay between profit, loss, and risk in PV exchanges is crucial for developing a comprehensive model of economic interactions. It helps explain why individuals might engage in seemingly irrational behaviors and why markets might not always function as efficiently as traditional economic theory would predict.

Evaluating Productive Power

If we sum the PV profit obtained through our lifetime of interactions, we arrive at our total PP. This concept of cumulative value creation aligns with the idea of human capital accumulation (Becker, 1964) and the theory of path dependence in economic outcomes (Arthur, 1989).

This total PP is a superset of society's current measurement of PP, referred to as monetary Net Worth. While traditional economic measures focus primarily on tangible assets and financial capital, our model proposes a more comprehensive view of an individual's productive capacity. This approach resonates with recent efforts to develop more inclusive measures of wealth and well-being, such as the Inclusive Wealth Index (UNU-IHDP and UNEP, 2014) and the OECD's Better Life Index (OECD, 2011).

Evaluating PP involves considering various forms of capital:

1.     Financial Capital: Traditional monetary assets and liabilities.

2.     Human Capital: Skills, knowledge, and experiences that enhance one's productive capacity (Schultz, 1961).

3.     Social Capital: Networks and relationships that facilitate value creation (Putnam, 2000).

4.     Cultural Capital: Non-financial social assets that promote social mobility (Bourdieu, 1986).

5.     Health Capital: Physical and mental well-being that affects productivity (Grossman, 1972).

6.     Natural Capital: Environmental resources and ecosystem services that contribute to human well-being (Costanza et al., 1997).

The challenge in evaluating PP lies in quantifying and aggregating these diverse forms of capital. Some components, like financial assets, are relatively straightforward to measure. Others, such as the value of one's social networks or the long-term benefits of education, are more difficult to quantify. This challenge is similar to the issues faced in measuring intangible assets in corporate valuation (Lev, 2001).

Moreover, the value of PP is not static but dynamic, influenced by changing societal needs, technological advancements, and economic conditions. For instance, the PP associated with certain skills may appreciate or depreciate over time, similar to the concept of skill-biased technological change in labor economics (Acemoglu, 2002).

To evaluate PP more comprehensively, we might consider:

1.     Developing new metrics that capture non-monetary forms of value creation.

2.     Incorporating subjective measures of well-being and life satisfaction (Diener et al., 1999).

3.     Assessing the potential for future value creation, not just current stocks of PP.

4.     Considering the contextual nature of PP, as its value may vary across different social and economic environments.

By broadening our understanding and measurement of PP, we can gain deeper insights into individual and societal progress, moving beyond the limitations of traditional economic indicators.

Magnifying Productive Value Information

An individual or group's increase or decrease in PP does not have a linear relationship with PV exchanged in interactions. Existing PP is "applied" to exchanged PV to achieve a new PP that may increase differently (both positively and negatively) than a linear exchange would achieve. This non-linear relationship aligns with the concept of increasing returns to scale in endogenous growth theory (Romer, 1986), which posits that certain factors can lead to accelerating economic growth.

Technology

It can be argued that innovation and invention are key characteristics that humans have in abundance when compared to other species. The result of innovation and invention is technology. Technology can greatly enhance PP. This perspective is consistent with the theory of technological progress as a driver of economic growth, as proposed by Solow (1956) in his neoclassical growth model.

Consider the former laborer with a hand shovel who now runs a steam shovel. He can now generate 30-100 times more PV output (work) than before. Consider the machine gunner who decimates attackers at the rate of many single rifled infantry. Consider the invention and use of the atomic bomb to destroy cities and end wars.

These examples illustrate the concept of capital deepening and its impact on labor productivity, a key component of growth accounting (Solow, 1957). They also reflect the idea of general-purpose technologies (Bresnahan & Trajtenberg, 1995), which have the potential to drastically alter societies through their impact on pre-existing economic and social structures.

Technology magnifies both individual and group PP in dramatic ways. This magnification effect is central to the idea of technology as a multiplier of human capabilities, a concept explored in detail by Brynjolfsson and McAfee (2014) in their work on the "second machine age."

Teamwork

Even simpler are the co-actions of two individuals that result in a PP that is greater than the sum of its parts. It is common knowledge that teamwork can achieve greater results than could be achieved from individuals themselves. The combined PP of several individuals allows greater exchanges of PV than the sum of the unique individual's PP. This concept aligns with theories of collective intelligence (Woolley et al., 2010) and the synergies that can arise from effective collaboration.

Individuals collectively hunting game for their dinner have increased PP as a result of their team interactions. This example reflects the economic benefits of specialization and division of labor, as famously described by Adam Smith (1776) in his seminal work "The Wealth of Nations."

Natural Selection and Survival of the Fittest Information

Nature has created a model where individuals with more PP can more effectively compete with individuals having lesser PP. This concept aligns with the principles of natural selection as first proposed by Darwin (1859) and later refined in evolutionary economics (Nelson & Winter, 1982).

A simple example is one individual having more money than another while both are competing in a potential interaction with a third individual. Individuals with more money can always out-compete individuals with less money and thus win in any competitive situation. This economic advantage reflects the concept of resource-based competition in strategic management (Barney, 1991).

Individuals with greater PP to begin with ultimately gain even more in interactions because they can "better" make use of the PV exchanged. This self-reinforcing cycle of advantage is similar to the concept of cumulative advantage in sociology, also known as the Matthew effect (Merton, 1968).

Another example is the disparity of physical strength between individuals. While less important in modern day society, physical strength meant the difference between survival and not for early man. This historical importance of physical attributes in survival and reproduction is a key tenet of evolutionary biology (Mayr, 1982).

Individuals with more education have greater PP than those with less education. We have certifications such as Bachelor's and Master's degrees which give us very rough, yet usable, information about an individual's education PP. This relates to human capital theory (Becker, 1964) and signaling theory in labor economics (Spence, 1973).

Finally, individuals who have greater parenting skills raise offspring that have a decided PP advantage over those parents with lesser skills (with single parent households having an even larger reduction in parenting capability). This intergenerational transmission of advantage is a topic of study in social mobility research (Corak, 2013).

Natural Selection

Individuals with greater PP out-compete other individuals over time. For example, individuals that express certain traits may better compete with others. It is believed, for example, that early humans that could stand on two legs could better see prey and predators. Taller individuals, and those associated with them, tended to survive better than short individuals because of this. They had greater PP because of their height. This example illustrates the concept of adaptive traits in evolutionary biology (Futuyma, 2009).

The process of natural selection in human societies, however, is more complex than in other species due to cultural evolution. As Boyd and Richerson (1985) argue, human behavior is a product of both genetic and cultural inheritance systems. The PP model proposed could potentially bridge these two systems by providing a common currency (PV) for measuring both biological and cultural fitness.

The Productive Power of Societies Information

A society's total PP is a summation of all its individual citizens' PP. This concept aligns with the macroeconomic view of national wealth and productivity (Kuznets, 1934). In addition, groups of individuals can hold collective PP, like in the case of corporations and governments. This collective aspect of PP resonates with theories of social capital (Putnam, 2000) and institutional economics (North, 1990).

The Importance of Societal Defense

It can be argued that the most important PP of any society is the ability to militarily protect itself during potential times of war. This perspective aligns with theories in international relations, particularly the realist school of thought (Waltz, 1979). Military actions can defend against attacks on a society's economy by other societies. These PP categories could subdivide into military defense, cyber-attack defense, and other attacks on an economy such as sanctions and embargos. This multi-faceted view of national defense reflects the concept of comprehensive national power (Pillsbury, 2000) and the evolving nature of security threats in the modern era (Nye, 2011).

In addition, during times of scarce natural resources, a society's military capability may allow it to make war on other societies in pursuit of natural resources or other goals. This aspect of societal PP relates to theories of resource wars and the "resource curse" in international political economy (Ross, 2004).

Why Some Demographics Fall Economically Behind Others

It is within the currently unmeasured category of PV that most interactions occur. For example, the PP of a parent is immense compared to a child and this transfer of PV takes decades to happen. This intergenerational transfer of PP aligns with theories of human capital formation (Becker & Tomes, 1986) and the economics of the family (Becker, 1981).

But what if some of these categories of interactions are missing from an individual's life? For example, how much PP does a child not gain because of a single parent household? There is a reason nature has given our species the opportunity of having differing genders come together and produce offspring. The raising of offspring by two differing gender parents transfers significantly more PV in decades of nurturing than happens in the case of a single parent upbringing. This perspective relates to research on family structure and child outcomes (McLanahan & Sandefur, 1994).

Are we saying one parent households are bad? No. But we are claiming that less PV exchanges occur and that resulting adults start their adulthood with significantly less PP than those from two differing gender households. This nuanced view of family structure impacts aligns with more recent research that considers multiple factors influencing child development and socioeconomic outcomes (Amato, 2005).

Examinations of societies and sub-societies reveal many categories of PV that must be exchanged in order for an individual to have the PP to survive and interact on one's own without guidance. When categories are missing in an individual's life, the correlated reduction in PP occurs. This concept of cumulative disadvantage resonates with theories of social stratification and the intergenerational transmission of inequality (Bowles & Gintis, 2002).

A society’s total PP is a summation of all its individual citizens PP.  In addition, groups of individuals can hold collective PP, like in the case of corporations and governments.

The Bridge to Behavioral Economics Information

Interactions are the lifeblood of human existence. Since we must decide where and when to exchange PV, we must draw on our "experiences". This decision-making process aligns closely with the foundations of behavioral economics, which seeks to understand how psychological, cognitive, emotional, and social factors influence economic decisions (Kahneman & Tversky, 1979).

What are experiences? The obvious answer is our memory of past interactions, but it's more complicated than that. What we store is the "analysis" of past experiences. This concept of memory as reconstruction rather than mere retrieval is supported by research in cognitive psychology (Schacter, 1999). When we "recall" memories, we really reconstruct them rather than reproduce them verbatim. This process of memory reconstruction has significant implications for decision-making, as explored in behavioral economics research on the availability heuristic and hindsight bias (Tversky & Kahneman, 1973).

Then any information about a possible exchange of PV is compared against our experiences to help us decide whether to engage in an exchange or not. This decision-making process reflects the concept of bounded rationality (Simon, 1955), which recognizes that individuals make decisions based on limited information and cognitive capacity.

Our summed experiences, or knowledge, represent our PP from which we can generate PV to exchange. This accumulation of knowledge and its impact on decision-making aligns with theories of human capital (Becker, 1964) and experiential learning (Kolb, 1984).

Where do we get the data to analyze before exchanging PV? We draw information from our many senses. We inspect sensed information and compare it against our experiences. This process of sensory input and information processing in decision-making is studied in neuroeconomics, which combines neuroscience, economics, and psychology to understand how the brain makes decisions (Glimcher & Fehr, 2013).

How we use our senses to construct our analysis is a wide topic that is beyond the scope of this paper. Nevertheless, we use our eyes and ears and other senses to gather data that represents the information about a particular PV in any interaction. This multi-sensory approach to information gathering and decision-making is explored in research on consumer behavior and marketing (Krishna, 2012).

Then we may or may not decide to apply PP and exchange PV. This final step in the decision-making process reflects the concept of expected utility theory (von Neumann & Morgenstern, 1944), as well as more recent developments in prospect theory (Kahneman & Tversky, 1979), which accounts for risk attitudes and framing effects in decision-making.

By linking the concepts of PV and PP to these established theories in behavioral economics and related fields, our model provides a bridge between traditional economic thinking and the more nuanced understanding of human behavior offered by behavioral economics.

The Bridge to Traditional Economics Information

This relationship is quite simple. Interactions that exchange PV, specifically in the form of currencies, are combined and tallied to produce our GDP. It's that simple. Every exchange in traditional economics, with all its rules and models, is an exchange of currency PV in an interaction. This concept aligns with the fundamental principles of national income accounting (Kuznets, 1934) and the circular flow model of the economy (Samuelson, 1948).

However, we will argue that the majority of PV exchanged in interactions comes not in the form of currencies but in exchanging the information category of PV. This perspective resonates with the growing recognition of the importance of information in economic theory, as highlighted by the economics of information (Stiglitz, 2000) and the knowledge economy (Drucker, 1969).

Certainly, there are many fields where information exchanged is monetized, but not generally. For example, all the PV exchanged with a child over the years by a parent currently is not measured in an arithmetic way, yet vast amounts of PV get exchanged over years to turn a child into an adult. This concept of non-market production and its economic value has been explored in the literature on household production (Becker, 1965) and the care economy (Folbre, 2006).

Our model bridges traditional economics by expanding the scope of what is considered valuable exchange. While traditional economics focuses primarily on market transactions, our model incorporates:

1.     Non-market exchanges: Such as knowledge transfer within families or communities, which contribute significantly to human capital formation (Coleman, 1988).

2.     Intangible assets: Including intellectual property, organizational capital, and social capital, which are increasingly recognized as crucial for economic growth (Corrado et al., 2009).

3.     Information flows: Recognizing that information itself can be a form of PV, not just a facilitator of transactions (Shapiro & Varian, 1998).

4.     Social interactions: Acknowledging that social relationships and networks have economic value, as explored in social capital theory (Putnam, 2000).

By incorporating these elements, our model provides a more comprehensive view of economic activity that aligns with recent efforts to go beyond GDP in measuring economic well-being and progress (Stiglitz et al., 2009).

Moreover, the PV model can help explain certain economic phenomena that traditional models struggle with:

1.     The value of free goods and services in the digital economy (Brynjolfsson & McAfee, 2014).

2.     The economic impact of social networks and online communities (Aral et al., 2013).

3.     The role of trust and reputation in facilitating economic exchanges (Fehr, 2009).

In essence, our model serves as a bridge by providing a framework that encompasses both the tangible, easily measurable exchanges that form the basis of traditional economic analysis, and the intangible, often overlooked exchanges that behavioral economics and other newer fields have highlighted as crucial to understanding economic behavior and outcomes.

Summary Information

Societies consist of individuals who must interact with each other to survive. These interactions exchange Productive Value (PV), defined as information, services, goods, and currencies. This concept of value exchange aligns with fundamental principles in economics (Smith, 1776) while extending beyond traditional market transactions to include non-market interactions (Becker, 1965).

The ability to generate PV comes from an individual's Productive Power (PP). PP stems from an individual's knowledge and physical abilities, resonating with human capital theory (Becker, 1964) and the resource-based view in strategic management (Barney, 1991).

Typical interactions are a two-way exchange of PV between two individuals, however multiple individuals or groups and multiple PV exchanges can be summed for an interaction. The granularity used is as needed for the desired insight into the particular PP category. This flexible approach to analyzing interactions reflects the complexity of economic and social exchanges recognized in network theory (Granovetter, 1973) and social exchange theory (Emerson, 1976).

Individuals profit or lose in interactions, thereby increasing or decreasing their PP net worth. This dynamic aligns with concepts in behavioral economics, particularly prospect theory (Kahneman & Tversky, 1979), which examines how individuals make decisions under conditions of risk and uncertainty.

Those individuals who profit more than others over time gain a competitive advantage in PP. Over longer periods, the PP advantages of some individuals will "select out" those individuals with lesser PP. This process of accumulation and selection mirrors aspects of evolutionary economics (Nelson & Winter, 1982) and the theory of cumulative advantage in sociology (Merton, 1968).

The Productive Value Model for Bridging Behavioral and Traditional Economics provides a comprehensive framework that:

1.     Incorporates both tangible and intangible forms of value exchange, addressing limitations in traditional economic measures (Stiglitz et al., 2009).

2.     Recognizes the role of information as both a facilitator of exchanges and a form of value itself, aligning with information economics (Stiglitz, 2000).

3.     Accounts for the cumulative effects of interactions on individual and societal productive capacity, reflecting theories of human and social capital (Coleman, 1988).

4.     Provides a bridge between traditional economic analysis and insights from behavioral economics, offering a more holistic view of economic behavior and outcomes.

By integrating these diverse perspectives, our model offers a novel approach to understanding the complex interplay of factors that drive economic interactions and societal progress.

Additional Topics for Examination Information

The following topics are offered as possible investigation avenues that could enhance the model.

Applicability to Species other than Humans

The Productive Value model for Interactions may describe the interactions of other species besides humans. Many animal species are social and depend on one another for survival, a concept well-established in evolutionary biology and behavioral ecology (Wilson, 1975).

Wolves hunt in packs, exchanging PV continually with their visually observed actions and vocal calls to other wolves. When one wolf sounds an alarm, it is exchanging information PV with other wolves who will respond respectively. When they chase prey, they do it collectively, interacting with each other as needed. When they kill prey, they do it collectively, as well. This cooperative behavior in wolves has been extensively studied and can be viewed through the lens of reciprocal altruism (Trivers, 1971) and kin selection (Hamilton, 1964).

Many animals steal PV from other animals, a behavior known as kleptoparasitism in ecology (Brockmann & Barnard, 1979). Consider the large fish that eats a smaller fish that just ate an even smaller fish. The theft of an individual's entire life has occurred, twice. Or take the case of a lion protecting its kill from thieving hyenas. These interactions can be analyzed using game theory models in evolutionary biology (Maynard Smith, 1982).

The application of our PV model to non-human species could provide insights into:

1.     Social Structure: How different species organize their societies based on PV exchanges (Clutton-Brock, 2016).

2.     Cooperation and Competition: The balance between cooperative PV exchanges and competitive or parasitic interactions within and between species (Nowak, 2006).

3.     Information Transfer: How information as a form of PV is shared in animal societies, such as in bee dances or bird songs (Von Frisch, 1967).

4.     Resource Allocation: How species manage and distribute resources, which can be viewed as a form of PV (Stephens & Krebs, 1986).

5.     Evolution of Social Behavior: How the exchange of PV might have influenced the evolution of social behaviors across different species (Dunbar, 1998).

By applying the PV model to other species, we may gain new perspectives on economic interactions in human societies. For instance, studying how other species manage risk and uncertainty in their PV exchanges could provide insights into human economic behavior under similar conditions.

Moreover, this cross-species application of the PV model could contribute to the growing field of animal economics, which applies economic models to understand animal behavior (Kalenscher & van Wingerden, 2011).

On Well Being and Mental Health

Traditional economics teaches us that achieving greater utility in exchanges leads to greater happiness, a concept rooted in utilitarian philosophy and economic theory (Bentham, 1789; Marshall, 1890). This is consistent with our model. Individuals who profit more than others and obtain greater PP are likely indeed happier than the individual who has lost everything. This relationship between economic outcomes and subjective well-being has been extensively studied in the field of happiness economics (Easterlin, 1974; Frey & Stutzer, 2002).

In fact, it is quite possible that their happiness is correlated to their recollection of non-profitable interactions as well as profitable interactions. This idea aligns with the peak-end rule in behavioral economics, which suggests that people's judgments of past experiences depend on peak and final moments rather than the sum of every moment of the experience (Kahneman et al., 1993).

It is widely accepted that Cognitive Behavioral Therapy (CBT) is one of the few mental health treatments that does indeed work in most cases (Butler et al., 2006). CBT teaches us to re-evaluate past experiences and recast them in more profitable, or happier, lights. This process of cognitive restructuring can be viewed as a way of reinterpreting past PV exchanges to increase current PP, potentially leading to more favorable future exchanges.

Another area to explore would be the notion of deleting bad memories in individuals, thereby erasing some of the very big losses in one's life (the most unprofitable interactions). While the complete erasure of memories remains in the realm of science fiction, research on memory reconsolidation offers insights into how traumatic memories might be modified or their emotional impact reduced (Nader et al., 2000).

The PV model's implications for mental health and well-being extend beyond these points:

1.     Social Connections: The model emphasizes the importance of interactions, which aligns with research showing the crucial role of social relationships in mental health and well-being (Holt-Lunstad et al., 2010).

2.     Productive Engagement: The concept of PP suggests that being able to contribute value can enhance well-being, consistent with findings on the psychological benefits of meaningful work and activities (Steger et al., 2012).

3.     Resilience: The model's focus on profiting from interactions over time relates to psychological resilience, or the ability to bounce back from adversity (Luthar et al., 2000).

4.     Cognitive Appraisal: The idea that individuals evaluate potential PV exchanges based on past experiences aligns with cognitive appraisal theories of emotion, which emphasize the role of interpretation in emotional responses (Lazarus, 1991).

5.     Skill Development: Increasing one's PP through learning and experience can be seen as a form of personal growth, which is associated with improved well-being (Ryff & Singer, 2008).

By viewing mental health and well-being through the lens of PV exchanges and PP accumulation, we may gain new insights into therapeutic approaches and interventions. This perspective could contribute to the growing field of positive psychology, which focuses on fostering human flourishing rather than merely treating mental illness (Seligman & Csikszentmihalyi, 2000).

Investigating and Quantifying Non-typical Productive Power Group Categories

As we have discussed, there are many PP categories that are already quantified through traditional economics. The greatest amounts of PV transferred in a society are in non-recorded interactions, however. This aligns with the concept of the "invisible economy" or "core economy" as described by Eisler (2007), which includes unpaid work, volunteerism, and household production.

We value the breadwinner in a family actuarially but do not measure his contributions in raising offspring. This relates to the challenges in measuring the economic value of parenting and household work, a topic explored in feminist economics (Folbre, 2001). We value the teacher by his salary while ignoring the impact of his or her teachings. This limitation in current economic measures has been highlighted in discussions about the shortcomings of GDP as a measure of societal progress (Stiglitz et al., 2009).

Trying to quantify some of these more intangible Power group categories would be well worth the effort. This aligns with recent efforts to measure intangible assets in economics (Corrado et al., 2009) and the growing field of social impact measurement (Nicholls, 2009).

"Parenting or child rearing is the process of promoting and supporting the physical, emotional, social, financial, and intellectual development of a child from infancy to adulthood. Parenting refers to the aspects of raising a child aside from the biological relationship." This definition from Wikipedia barely scratches the surface of the PP passed on to a child during their lifetime. We need to understand this category better (which should help understanding why single-parent demographics sometimes fall behind others).

The economic value of parenting and its impact on child outcomes has been studied in various contexts:

1.     Human Capital Formation: Research has shown the crucial role of parental investments in children's cognitive and non-cognitive skill development (Cunha & Heckman, 2007).

2.     Intergenerational Mobility: Studies have examined how parental resources and behaviors affect children's future economic outcomes (Corak, 2013).

3.     Time Use: Economists have attempted to quantify the time parents spend on childcare and its economic value (Folbre & Yoon, 2007).

4.     Family Structure: Research has explored how different family structures, including single-parent households, affect children's outcomes (McLanahan & Sandefur, 1994).

To better quantify these non-typical PP categories, we might consider:

1.     Developing new metrics that capture the long-term impact of parenting on children's future PP.

2.     Utilizing time-use surveys to estimate the quantity and quality of parent-child interactions.

3.     Incorporating measures of social and emotional skills development into our understanding of PP transfer from parents to children.

4.     Exploring the role of extended family networks and community support in supplementing parental PP transfer.

5.     Investigating the economic value of mentorship and role modeling, both within and outside the family unit.

By expanding our understanding and measurement of these non-typical PP categories, we can gain a more comprehensive view of how value is created and transferred in society, potentially informing more effective social and economic policies.

Examining the Productive Power of Past Competing Societies

As we become more literate with respect to measuring the PP of individuals and knowing that a society's PP is a summation of its individuals' PP, can we better quantify the total of PP past competing societies and analyze their advances and declines with respect to competing societies? This approach aligns with the field of cliometrics, which applies economic theory and quantitative methods to the study of economic history (Fogel & Engerman, 1974).

For example, societies with greater PP should out compete societies with less in both trade and war. Or more correctly, societies that apply more PP should outcompete societies that apply less in their summed interactions. This concept resonates with theories of societal competition and collapse (Diamond, 2005; Tainter, 1988), as well as with studies on the rise and fall of great powers (Kennedy, 1987).

Applying the PP model to historical societies could provide insights into:

1.     Technological Advancement: How innovations in technology contributed to a society's overall PP, similar to the concept of general-purpose technologies in economic history (Bresnahan & Trajtenberg, 1995).

2.     Institutional Development: The role of institutions in enhancing or hindering a society's PP, as explored in institutional economics (North, 1990).

3.     Human Capital: How education, skills, and knowledge transfer contributed to a society's PP, relating to theories of human capital in economic growth (Becker, 1964).

4.     Social Organization: The impact of social structures and norms on a society's ability to generate and apply PP, as studied in economic sociology (Granovetter, 1985).

5.     Resource Management: How societies' management of natural resources affected their long-term PP, connecting to theories of sustainable development (Ostrom, 1990).

6.     Cultural Factors: The influence of cultural values and beliefs on a society's PP, as explored in cultural economics (Guiso et al., 2006).

7.     Military Capabilities: How military power contributed to overall societal PP, relating to theories of military effectiveness (Biddle, 2004).

8.     Trade Networks: The role of trade in enhancing or diminishing a society's PP relative to its competitors, as studied in economic history (Findlay & O'Rourke, 2007).

By applying the PP model to historical societies, we might gain new insights into:

1.     The factors that led to the rise and fall of civilizations.

2.     The role of technological innovation in societal competition.

3.     The importance of social and institutional structures in maintaining societal PP.

4.     The long-term consequences of resource exploitation and management.

5.     The impact of cultural values on economic and military success.

This historical application of the PP model could contribute to our understanding of current global economic and political dynamics, providing valuable lessons for contemporary policymaking and strategic planning.

Further Mathematical Investigation

We have only introduced the minimum amount of mathematics in the model currently. The model should be mathematically linked to existing economic theory. This approach aligns with the field of mathematical economics, which uses mathematical methods to represent economic theories and analyze economic phenomena (Debreu, 1959).

Potential areas for further mathematical investigation include:

1.     Formalizing the PV Exchange Equation: The current equation (NewPP1 = ExistingPP1 + PP1(PV2) - PV1) could be further developed and refined. This could involve incorporating concepts from utility theory (von Neumann & Morgenstern, 1944) and production functions in economics (Cobb & Douglas, 1928).

2.     Modeling PP Accumulation Over Time: Developing differential equations to model how PP changes over time, similar to models of human capital accumulation (Ben-Porath, 1967) or endogenous growth theory (Romer, 1990).

3.     Game Theoretical Approaches: Applying game theory to model strategic interactions between individuals or groups with different levels of PP, building on the work of Nash (1951) in non-cooperative game theory.

4.     Network Models of PV Exchange: Using graph theory and network analysis to model the flow of PV through social and economic networks, inspired by research in social network analysis (Wasserman & Faust, 1994) and economic networks (Jackson, 2010).

5.     Stochastic Processes in PP Development: Incorporating randomness and uncertainty into the model using stochastic processes, similar to approaches in financial economics (Black & Scholes, 1973).

6.     Optimization of PP Allocation: Developing mathematical models for optimal allocation of PP across different activities or investments, drawing on techniques from operations research and mathematical programming (Dantzig, 1963).

7.     Dynamic Systems Modeling: Using systems of differential equations to model the interplay between different forms of PP and PV in a society, inspired by system dynamics approaches (Forrester, 1961).

8.     Agent-Based Modeling: Developing computational models to simulate PV exchanges and PP accumulation in large populations of heterogeneous agents, building on work in agent-based computational economics (Tesfatsion, 2002).

9.     Entropy and Information Theory: Exploring the application of concepts from information theory to quantify the information content of PV exchanges, inspired by the use of entropy in economics (Theil, 1967).

1.     Fractal Analysis: Investigating whether PP distribution and accumulation exhibit fractal properties, similar to studies of fractal patterns in financial markets (Mandelbrot, 1997).

By developing these mathematical aspects of the model, we can:

1.     Increase the model's predictive power and testability.

2.     Facilitate integration with existing economic models and theories.

3.     Provide a more rigorous foundation for policy recommendations based on the model.

4.     Enable more sophisticated simulations of PV exchanges and PP accumulation in various scenarios.

This mathematical development of the PV/PP model could contribute to bridging gaps between different schools of economic thought and provide new tools for analyzing complex economic phenomena.

Further Analysis of Information within the Model

The model currently simplifies "information" as "a descriptor of PV" and/or "PV itself". When information is one or the other (or both) is vague at best. Further clarification of exactly how information is germane to the model is warranted. This need for deeper analysis aligns with the growing recognition of information as a crucial economic resource, as highlighted in the economics of information (Stiglitz, 2000).

To further develop the role of information within the PV/PP model, we could explore:

1.     Information as a Commodity: Analyzing how information itself can be valued and exchanged as PV, building on work in the economics of information goods (Shapiro & Varian, 1998).

2.     Information Asymmetry: Examining how differences in information possession affect PV exchanges and PP accumulation, drawing on theories of asymmetric information in markets (Akerlof, 1970).

3.     Information Processing Capacity: Investigating how an individual's ability to process and utilize information affects their PP, relating to concepts of bounded rationality (Simon, 1955) and information processing in decision making (Kahneman, 2011).

4.     Information Networks: Analyzing how information flows through social and economic networks, affecting PV exchanges and PP distribution, inspired by research in social network analysis (Granovetter, 1973) and information diffusion (Rogers, 2003).

5.     Information and Uncertainty: Exploring how incomplete or imperfect information introduces uncertainty into PV exchanges, drawing on economic theories of decision making under uncertainty (Knight, 1921).

6.     Information and Innovation: Examining how new information leads to innovation and potentially creates new forms of PV and PP, relating to theories of knowledge spillovers in endogenous growth models (Romer, 1990).

7.     Information Entropy: Applying concepts from information theory to quantify the information content of PV exchanges and its relation to economic value (Theil, 1967).

8.     Signaling and Screening: Analyzing how information is used in signaling quality or screening in PV exchanges, building on signaling theory in economics (Spence, 1973).

9.     Information and Trust: Investigating the role of information in building trust and facilitating PV exchanges, drawing on research in social capital (Coleman, 1988) and the economics of trust (Fehr, 2009).

10.  Information Overload: Examining how an excess of information might affect PP and decision-making in PV exchanges, relating to research on choice overload (Schwartz, 2004) and attention economics (Davenport & Beck, 2001).

By deepening our understanding of information within the PV/PP model, we can:

1.     More accurately represent the complexities of modern information-based economies.

2.     Better explain phenomena such as the value of data in the digital economy.

3.     Provide insights into the role of education and lifelong learning in PP accumulation.

4.     Offer new perspectives on the importance of information infrastructure for economic development.

This enhanced treatment of information within the model could contribute to bridging gaps between information economics, behavioral economics, and traditional economic theory, providing a more comprehensive framework for understanding value creation and exchange in the information age.

Separated Parent Families

Could there be a simple correlation between attributes such as number of yearly visitations and higher individual outcomes? This question aligns with research on the impact of non-resident parent involvement on child outcomes (Amato & Gilbreth, 1999). This would potentially quantify the PP exchanges to and from a child as a function of simple time spent together with a parent (it's not quality time but perhaps just time).

Exploring this aspect of the PV/PP model could involve:

1.     Frequency of Contact: Analyzing how the frequency of visitations or interactions with a non-resident parent affects a child's PP accumulation. This relates to studies on the quantity of father involvement and child well-being (Lamb, 2004).

2.     Quality of Interactions: Investigating whether the nature of the interactions (e.g., recreational, educational, or caregiving) influences PP transfer. This aligns with research on the quality of parent-child relationships in divorced families (Amato, 2000).

3.     Consistency Over Time: Examining the impact of consistent, long-term involvement versus sporadic contact. This connects to longitudinal studies on the effects of divorce on children (Wallerstein & Lewis, 2004).

4.     Age-dependent Effects: Analyzing whether the impact of parental separation and subsequent visitation patterns varies with the child's age. This relates to developmental perspectives on children's adjustment to divorce (Hetherington & Kelly, 2002).

5.     Gender Differences: Investigating whether the effects of separated parent involvement differ for boys and girls. This aligns with research on gender-specific outcomes in children of divorce (Videon, 2005).

6.     Technology-mediated Contact: Examining the role of digital communication in maintaining PP exchanges between non-resident parents and children. This connects to studies on virtual parenting and its impact (Yarosh et al., 2009).

7.     Extended Family Involvement: Analyzing how the involvement of grandparents or other extended family members might compensate for reduced contact with a non-resident parent. This relates to research on the role of extended kin in divorced families (Jappens & Van Bavel, 2016).

8.     Socioeconomic Factors: Investigating how socioeconomic status interacts with visitation patterns to affect PP exchanges. This aligns with studies on the intersection of family structure and social class (McLanahan & Percheski, 2008).

9.      

By applying the PV/PP model to separated parent families, we could:

1.     Develop more nuanced understanding of how family structure affects child outcomes.

2.     Inform custody and visitation policies to maximize positive PP exchanges.

3.     Design interventions to enhance PP transfer in separated families.

4.     Contribute to the broader discussion on work-life balance and parental involvement.

This analysis could provide valuable insights for family law, social policy, and child development research, potentially leading to more effective strategies for supporting children in separated parent families.

Analysis of Militaries

One area to apply the model is in evaluation the power of militaries. It is claimed that a military's strength is only 1/3 armaments and such and 2/3 morale, or the will to fight. This perspective aligns with classical military theory, particularly the works of Clausewitz (1832), who emphasized the importance of moral factors in warfare.

In addition, some weapons end up being more decisive than their cost would suggest (shoulder fired anti-tank missiles and spotting drones). This concept relates to the theory of asymmetric warfare and the impact of disruptive military technologies (Arreguín-Toft, 2001).

When one takes a measurable account of the vast stores of PP exchangeable PV in a nation's military, including things such as morale, one could better analyze competitive militaries. This approach resonates with modern methods of military effectiveness analysis (Biddle, 2004) and the concept of comprehensive national power (Pillsbury, 2000).

During wartime, all of the PV exchanges (the shots fired, the soldiers killed, the bombs dropped, etc.) could be tracked day to day for both armies allowing enhanced planning for any mission or campaign. This idea aligns with contemporary approaches to military operations research and wargaming (Washburn & Kress, 2009).

Applying the PV/PP model to military analysis could involve:

1.     Quantifying Morale: Developing metrics to measure and compare the morale (as a form of PP) of different military units or entire armed forces (Britt & Dickinson, 2006).

2.     Weapon System Effectiveness: Analyzing the PV exchange rate of various weapon systems, considering both their direct and indirect effects (Dupuy, 1979).

3.     Training and Doctrine: Evaluating how different training methods and military doctrines affect the PP of military units (Posen, 1984).

4.     Leadership Impact: Assessing the multiplicative effect of leadership on the PP of military units (Wong et al., 2003).

5.     Logistics and Support: Analyzing how logistical capabilities and support structures contribute to a military's overall PP (Van Creveld, 2004).

6.     Information Warfare: Evaluating the impact of information operations and cyber warfare capabilities on military PP (Libicki, 2007).

7.     Alliance Structures: Assessing how military alliances affect the collective PP of allied forces (Walt, 1987).

8.     Economic Base: Analyzing the relationship between a nation's economic PP and its military capabilities (Kennedy, 1987).

9.     Technological Innovation: Evaluating how advances in military technology translate into changes in PP (Boot, 2006).

10.  Human Capital: Assessing the impact of personnel quality, education, and specialization on military PP (Biddle & Long, 2004).

By applying the PV/PP model to military analysis, we could:

1.     Develop more comprehensive measures of military power that go beyond simple counts of personnel and equipment.

2.     Better predict outcomes of military confrontations and conflicts.

3.     Inform defense policy and military procurement decisions.

4.     Enhance understanding of the relationship between societal PP and military capabilities.

This application of the model could contribute to both military science and broader international relations theory, potentially offering new insights into the nature of power in the international system.

References

1.     Acemoglu, D. (2002). Technical Change, Inequality, and the Labor Market. Journal of Economic Literature, 40(1), 7-72.

2.     Akerlof, G. A. (1970). The Market for "Lemons": Quality Uncertainty and the Market Mechanism. The Quarterly Journal of Economics, 84(3), 488-500.

3.     Amato, P. R. (2000). The Consequences of Divorce for Adults and Children. Journal of Marriage and Family, 62(4), 1269-1287.

4.     Amato, P. R. (2005). The Impact of Family Formation Change on the Cognitive, Social, and Emotional Well-Being of the Next Generation. The Future of Children, 15(2), 75-96.

5.     Amato, P. R., & Gilbreth, J. G. (1999). Nonresident Fathers and Children's Well-Being: A Meta-Analysis. Journal of Marriage and the Family, 61(3), 557-573.

6.     Aral, S., Dellarocas, C., & Godes, D. (2013). Introduction to the Special Issue—Social Media and Business Transformation: A Framework for Research. Information Systems Research, 24(1), 3-13.

7.     Arreguín-Toft, I. (2001). How the Weak Win Wars: A Theory of Asymmetric Conflict. International Security, 26(1), 93-128.

8.     Arrow, K. J. (1951). Social Choice and Individual Values. John Wiley & Sons.

9.     Arrow, K. J. (1972). Gifts and Exchanges. Philosophy & Public Affairs, 1(4), 343-362.

10.  Arrow, K. J. (1996). The Economics of Information: An Exposition. Empirica, 23(2), 119-128.

11.  Arthur, W. B. (1989). Competing Technologies, Increasing Returns, and Lock-In by Historical Events. The Economic Journal, 99(394), 116-131.

12.  Barney, J. (1991). Firm Resources and Sustained Competitive Advantage. Journal of Management, 17(1), 99-120.

13.  Becker, G. S. (1964). Human Capital: A Theoretical and Empirical Analysis, with Special Reference to Education. University of Chicago Press.

14.  Becker, G. S. (1965). A Theory of the Allocation of Time. The Economic Journal, 75(299), 493-517.

15.  Becker, G. S. (1976). The Economic Approach to Human Behavior. University of Chicago Press.

16.  Becker, G. S. (1981). A Treatise on the Family. Harvard University Press.

17.  Becker, G. S., & Tomes, N. (1986). Human Capital and the Rise and Fall of Families. Journal of Labor Economics, 4(3, Part 2), S1-S39.

18.  Ben-Porath, Y. (1967). The Production of Human Capital and the Life Cycle of Earnings. Journal of Political Economy, 75(4), 352-365.

19.  Bentham, J. (1789). An Introduction to the Principles of Morals and Legislation. T. Payne and Son.

20.  Biddle, S. (2004). Military Power: Explaining Victory and Defeat in Modern Battle. Princeton University Press.

21.  Biddle, S., & Long, S. (2004). Democracy and Military Effectiveness: A Deeper Look. Journal of Conflict Resolution, 48(4), 525-546.

22.  Black, F., & Scholes, M. (1973). The Pricing of Options and Corporate Liabilities. Journal of Political Economy, 81(3), 637-654.

23.  Boot, M. (2006). War Made New: Technology, Warfare, and the Course of History, 1500 to Today. Gotham Books.

24.  Bourdieu, P. (1986). The Forms of Capital. In J. Richardson (Ed.), Handbook of Theory and Research for the Sociology of Education (pp. 241-258). Greenwood.

25.  Bowles, S., & Gintis, H. (2002). The Inheritance of Inequality. Journal of Economic Perspectives, 16(3), 3-30.

26.  Boyd, R., & Richerson, P. J. (1985). Culture and the Evolutionary Process. University of Chicago Press.

27.  Bresnahan, T. F., & Trajtenberg, M. (1995). General purpose technologies 'Engines of growth'? Journal of Econometrics, 65(1), 83-108.

28.  Britt, T. W., & Dickinson, J. M. (2006). Morale During Military Operations: A Positive Psychology Approach. In T. W. Britt, C. A. Castro, & A. B. Adler (Eds.), Military Life: The Psychology of Serving in Peace and Combat (Vol. 1, pp. 157-184). Praeger Security International.

29.  Brockmann, H. J., & Barnard, C. J. (1979). Kleptoparasitism in birds. Animal Behaviour, 27, 487-514.

30.  Brynjolfsson, E., & McAfee, A. (2014). The Second Machine Age: Work, Progress, and Prosperity in a Time of Brilliant Technologies. W. W. Norton & Company.

31.  Buchanan, J. M. (1964). What Should Economists Do? Southern Economic Journal, 30(3), 213-222.

32.  Butler, A. C., Chapman, J. E., Forman, E. M., & Beck, A. T. (2006). The empirical status of cognitive-behavioral therapy: A review of meta-analyses. Clinical Psychology Review, 26(1), 17-31.

33.  Clausewitz, C. von. (1832/1984). On War. (M. Howard & P. Paret, Trans.). Princeton University Press.

34.  Clutton-Brock, T. (2016). Mammal Societies. John Wiley & Sons.

35.  Coase, R. H. (1960). The Problem of Social Cost. The Journal of Law and Economics, 3, 1-44.

36.  Cobb, C. W., & Douglas, P. H. (1928). A Theory of Production. American Economic Review, 18(1), 139-165.

37.  Coleman, J. S. (1988). Social Capital in the Creation of Human Capital. American Journal of Sociology, 94, S95-S120.

38.  Corak, M. (2013). Income Inequality, Equality of Opportunity, and Intergenerational Mobility. Journal of Economic Perspectives, 27(3), 79-102.

39.  Corrado, C., Hulten, C., & Sichel, D. (2009). Intangible Capital and U.S. Economic Growth. Review of Income and Wealth, 55(3), 661-685.

40.  Costanza, R., d'Arge, R., de Groot, R., Farber, S., Grasso, M., Hannon, B., Limburg, K., Naeem, S., O'Neill, R. V., Paruelo, J., Raskin, R. G., Sutton, P., & van den Belt, M. (1997). The Value of the World's Ecosystem Services and Natural Capital. Nature, 387, 253-260.

41.  Cunha, F., & Heckman, J. J. (2007). The Technology of Skill Formation. American Economic Review, 97(2), 31-47.

42.  Dantzig, G. B. (1963). Linear Programming and Extensions. Princeton University Press.

43.  Darwin, C. (1859). On the Origin of Species by Means of Natural Selection, or the Preservation of Favoured Races in the Struggle for Life. John Murray.

44.  Davenport, T. H., & Beck, J. C. (2001). The Attention Economy: Understanding the New Currency of Business. Harvard Business Press.

45.  Debreu, G. (1959). Theory of Value: An Axiomatic Analysis of Economic Equilibrium. Yale University Press.

46.  Diener, E., Suh, E. M., Lucas, R. E., & Smith, H. L. (1999). Subjective Well-Being: Three Decades of Progress. Psychological Bulletin, 125(2), 276-302.

47.  Drucker, P. F. (1969). The Age of Discontinuity: Guidelines to Our Changing Society. Harper & Row.

48.  Dunbar, R. I. M. (1998). The social brain hypothesis. Evolutionary Anthropology, 6(5), 178-190.

49.  Dupuy, T. N. (1979). Numbers, Predictions and War: Using History to Evaluate Combat Factors and Predict the Outcome of Battles. Heroes Books.

50.  Easterlin, R. A. (1974). Does economic growth improve the human lot? Some empirical evidence. In P. A. David & M. W. Reder (Eds.), Nations and Households in Economic Growth (pp. 89-125). Academic Press.

51.  Edvinsson, L., & Malone, M. S. (1997). Intellectual Capital: Realizing Your Company's True Value by Finding Its Hidden Brainpower. HarperBusiness.

52.  Edvinsson, L., & Malone, M. S. (1997). Intellectual Capital: Realizing Your Company's True Value by Finding Its Hidden Brainpower. HarperBusiness.

53.  Eisler, R. (2007). The Real Wealth of Nations: Creating a Caring Economics. Berrett-Koehler Publishers.

54.  Emerson, R. M. (1976). Social Exchange Theory. Annual Review of Sociology, 2, 335-362.

55.  Fehr, E. (2009). On the Economics and Biology of Trust. Journal of the European Economic Association, 7(2-3), 235-266.

56.  Folbre, N. (2001). The Invisible Heart: Economics and Family Values. The New Press.

57.  Folbre, N. (2006). Measuring Care: Gender, Empowerment, and the Care Economy. Journal of Human Development, 7(2), 183-199.

58.  Folbre, N., & Yoon, J. (2007). What is Child Care? Lessons from Time-Use Surveys of Major English-Speaking Countries. Review of Economics of the Household, 5(3), 223-248.

59.  Forrester, J. W. (1961). Industrial Dynamics. MIT Press.

60.  Frey, B. S., & Stutzer, A. (2002). What can economists learn from happiness research? Journal of Economic Literature, 40(2), 402-435.

61.  Futuyma, D. J. (2009). Evolution (2nd ed.). Sinauer Associates.

62.  Glimcher, P. W., & Fehr, E. (Eds.). (2013). Neuroeconomics: Decision Making and the Brain (2nd ed.). Academic Press.

63.  Granovetter, M. S. (1973). The Strength of Weak Ties. American Journal of Sociology, 78(6), 1360-1380.

64.  Grant, R. M. (1996). Toward a knowledge-based theory of the firm. Strategic Management Journal, 17(S2), 109-122.

65.  Grossman, M. (1972). On the Concept of Health Capital and the Demand for Health. Journal of Political Economy, 80(2), 223-255.

66.  Hamilton, W. D. (1964). The genetical evolution of social behaviour. I. Journal of Theoretical Biology, 7(1), 1-16.

67.  Hanushek, E. A. (2011). The Economic Value of Higher Teacher Quality. Economics of Education Review, 30(3), 466-479.

68.  Hartley, K., & Sandler, T. (1995). Handbook of Defense Economics. Elsevier.

69.  Henrich, J., Boyd, R., Bowles, S., Camerer, C., Fehr, E., Gintis, H., & McElreath, R. (2001). In Search of Homo Economicus: Behavioral Experiments in 15 Small-Scale Societies. American Economic Review, 91(2), 73-78.

70.  Hetherington, E. M., & Kelly, J. (2002). For Better or For Worse: Divorce Reconsidered. W. W. Norton & Company.

71.  Holt-Lunstad, J., Smith, T. B., & Layton, J. B. (2010). Social relationships and mortality risk: A meta-analytic review. PLoS Medicine, 7(7), e1000316.

72.  Homans, G. C. (1958). Social Behavior as Exchange. American Journal of Sociology, 63(6), 597-606.

73.  Jackson, M. O. (2010). Social and Economic Networks. Princeton University Press.

74.  Jappens, M., & Van Bavel, J. (2016). Parental Divorce, Residence Arrangements, and Contact Between Grandchildren and Grandparents. Journal of Marriage and Family, 78(2), 451-467.

75.  Jensen, M. C., & Meckling, W. H. (1976). Theory of the Firm: Managerial Behavior, Agency Costs and Ownership Structure. Journal of Financial Economics, 3(4), 305-360.

76.  Kahneman, D. (2011). Thinking, Fast and Slow. Farrar, Straus and Giroux.

77.  Kahneman, D., & Tversky, A. (1973). On the Psychology of Prediction. Psychological Review, 80(4), 237-251.

78.  Kahneman, D., & Tversky, A. (1979). Prospect Theory: An Analysis of Decision under Risk. Econometrica, 47(2), 263-291.

79.  Kahneman, D., Fredrickson, B. L., Schreiber, C. A., & Redelmeier, D. A. (1993). When more pain is preferred to less: Adding a better end. Psychological Science, 4(6), 401-405.

80.  Kalenscher, T., & van Wingerden, M. (2011). Why we should use animals to study economic decision making – a perspective. Frontiers in Neuroscience, 5, 82.

81.  Kennedy, P. (1987). The Rise and Fall of the Great Powers: Economic Change and Military Conflict from 1500 to 2000. Random House.

82.  Knight, F. H. (1921). Risk, Uncertainty and Profit. Houghton Mifflin.

83.  Kolb, D. A. (1984). Experiential Learning: Experience as the Source of Learning and Development. Prentice-Hall.

84.  Krishna, A. (2012). An Integrative Review of Sensory Marketing: Engaging the Senses to Affect Perception, Judgment and Behavior. Journal of Consumer Psychology, 22(3), 332-351.

85.  Kuznets, S. (1934). National Income, 1929-1932. 73rd US Congress, 2d session, Senate document no. 124, page 7.

86.  Lamb, M. E. (2004). The Role of the Father in Child Development. John Wiley & Sons.

87.  Lazarus, R. S. (1991). Emotion and Adaptation. Oxford University Press.

88.  Lev, B. (2001). Intangibles: Management, Measurement, and Reporting. Brookings Institution Press.

89.  Libicki, M. C. (2007). Conquest in Cyberspace: National Security and Information Warfare. Cambridge University Press.

90.  Loewenstein, G., & Prelec, D. (1992). Anomalies in Intertemporal Choice: Evidence and an Interpretation. The Quarterly Journal of Economics, 107(2), 573-597.

91.  Luthar, S. S., Cicchetti, D., & Becker, B. (2000). The construct of resilience: A critical evaluation and guidelines for future work. Child Development, 71(3), 543-562.

92.  Mandelbrot, B. B. (1997). Fractals and Scaling in Finance: Discontinuity, Concentration, Risk. Springer.

93.  Marshall, A. (1890). Principles of Economics. Macmillan and Co.

94.  Maynard Smith, J. (1982). Evolution and the Theory of Games. Cambridge University Press.

95.  Mayr, E. (1982). The Growth of Biological Thought: Diversity, Evolution, and Inheritance. Harvard University Press.

96.  McLanahan, S., & Percheski, C. (2008). Family Structure and the Reproduction of Inequalities. Annual Review of Sociology, 34, 257-276.

97.  Menger, C. (1871). Principles of Economics. Ludwig von Mises Institute.

98.  Merton, R. K. (1968). The Matthew Effect in Science. Science, 159(3810), 56-63.

99.  Muth, J. F. (1961). Rational Expectations and the Theory of Price Movements. Econometrica, 29(3), 315-335.

100.                 Nader, K., Schafe, G. E., & Le Doux, J. E. (2000). Fear memories require protein synthesis in the amygdala for reconsolidation after retrieval. Nature, 406(6797), 722-726.

101.                 Nash, J. (1951). Non-Cooperative Games. Annals of Mathematics, 54(2), 286-295.

102.                 Nelson, R. R., & Winter, S. G. (1982). An Evolutionary Theory of Economic Change. Harvard University Press.

103.                 Nicholls, A. (2009). 'We Do Good Things, Don't We?': 'Blended Value Accounting' in Social Entrepreneurship. Accounting, Organizations and Society, 34(6-7), 755-769.

104.                 North, D. C. (1990). Institutions, Institutional Change and Economic Performance. Cambridge University Press.

105.                 Nowak, M. A. (2006). Five rules for the evolution of cooperation. Science, 314(5805), 1560-1563.

106.                 Nye, J. S. (2011). The Future of Power. PublicAffairs.

107.                 OECD (2011). How's Life?: Measuring Well-being. OECD Publishing.

108.                 Pigou, A. C. (1920). The Economics of Welfare. Macmillan and Co.

109.                 Pillsbury, M. (2000). China Debates the Future Security Environment. National Defense University Press.

110.                 Posen, B. R. (1984). The Sources of Military Doctrine: France, Britain, and Germany Between the World Wars. Cornell University Press.

111.                 Pratt, J. W. (1964). Risk Aversion in the Small and in the Large. Econometrica, 32(1/2), 122-136.

112.                 Putnam, R. D. (2000). Bowling Alone: The Collapse and Revival of American Community. Simon & Schuster.

113.                 Rogers, E. M. (2003). Diffusion of Innovations (5th ed.). Free Press.

114.                 Romer, P. M. (1986). Increasing Returns and Long-Run Growth. Journal of Political Economy, 94(5), 1002-1037.

115.                 Romer, P. M. (1990). Endogenous Technological Change. Journal of Political Economy, 98(5, Part 2), S71-S102.

116.                 Romer, P. M. (1990). Endogenous Technological Change. Journal of Political Economy, 98(5, Part 2), S71-S102.

117.                 Romer, P. M. (1994). The Origins of Endogenous Growth. Journal of Economic Perspectives, 8(1), 3-22.

118.                 Ross, M. L. (2004). What Do We Know about Natural Resources and Civil War? Journal of Peace Research, 41(3), 337-356.

119.                 Ryff, C. D., & Singer, B. H. (2008). Know thyself and become what you are: A eudaimonic approach to psychological well-being. Journal of Happiness Studies, 9(1), 13-39.

120.                 Samuelson, P. A. (1948). Economics: An Introductory Analysis. McGraw-Hill.

121.                 Schacter, D. L. (1999). The Seven Sins of Memory: Insights from Psychology and Cognitive Neuroscience. American Psychologist, 54(3), 182-203.

122.                 Schelling, T. C. (1966). Arms and Influence. Yale University Press.

123.                 Schultz, T. W. (1961). Investment in Human Capital. The American Economic Review, 51(1), 1-17.

124.                 Schumpeter, J. A. (1942). Capitalism, Socialism and Democracy. Harper & Brothers.

125.                 Schwartz, B. (2004). The Paradox of Choice: Why More Is Less. Ecco.

126.                 Seligman, M. E. P., & Csikszentmihalyi, M. (2000). Positive psychology: An introduction. American Psychologist, 55(1), 5-14.

127.                 Shapiro, C., & Varian, H. R. (1998). Information Rules: A Strategic Guide to the Network Economy. Harvard Business School Press.

128.                 Simon, H. A. (1955). A Behavioral Model of Rational Choice. The Quarterly Journal of Economics, 69(1), 99-118.

129.                 Smith, A. (1776). An Inquiry into the Nature and Causes of the Wealth of Nations. W. Strahan and T. Cadell.

130.                 Smith, A. (1776). An Inquiry into the Nature and Causes of the Wealth of Nations. W. Strahan and T. Cadell.

131.                 Smith, A. (1776). An Inquiry into the Nature and Causes of the Wealth of Nations. W. Strahan and T. Cadell.

132.                 Solow, R. M. (1956). A Contribution to the Theory of Economic Growth. The Quarterly Journal of Economics, 70(1), 65-94.

133.                 Solow, R. M. (1957). Technical Change and the Aggregate Production Function. The Review of Economics and Statistics, 39(3), 312-320.

134.                 Spence, M. (1973). Job Market Signaling. The Quarterly Journal of Economics, 87(3), 355-374.

135.                 Steger, M. F., Dik, B. J., & Duffy, R. D. (2012). Measuring meaningful work: The work and meaning inventory (WAMI). Journal of Career Assessment, 20(3), 322-337.

136.                 Stephens, D. W., & Krebs, J. R. (1986). Foraging Theory. Princeton University Press.

137.                 Stiglitz, J. E. (2000). The Contributions of the Economics of Information to Twentieth Century Economics. The Quarterly Journal of Economics, 115(4), 1441-1478.

138.                 Stiglitz, J. E., Sen, A., & Fitoussi, J. P. (2009). Report by the Commission on the Measurement of Economic Performance and Social Progress. Commission on the Measurement of Economic Performance and Social Progress.

139.                 Tesfatsion, L. (2002). Agent-Based Computational Economics: Growing Economies from the Bottom Up. Artificial Life, 8(1), 55-82.

140.                 Thaler, R. H. (2015). Misbehaving: The Making of Behavioral Economics. W. W. Norton & Company.

141.                 Theil, H. (1967). Economics and Information Theory. North-Holland.

142.                 Trivers, R. L. (1971). The evolution of reciprocal altruism. The Quarterly Review of Biology, 46(1), 35-57.

143.                 Tversky, A., & Kahneman, D. (1973). Availability: A Heuristic for Judging Frequency and Probability. Cognitive Psychology, 5(2), 207-232.

144.                 UNU-IHDP and UNEP (2014). Inclusive Wealth Report 2014. Measuring Progress Toward Sustainability. Cambridge University Press.

145.                 Van Creveld, M. (2004). Supplying War: Logistics from Wallenstein to Patton (2nd ed.). Cambridge University Press.

146.                 Videon, T. M. (2005). Parent-Child Relations and Children's Psychological Well-Being: Do Dads Matter? Journal of Family Issues, 26(1), 55-78.

147.                 Von Frisch, K. (1967). The Dance Language and Orientation of Bees. Harvard University Press.

148.                 von Neumann, J., & Morgenstern, O. (1944). Theory of Games and Economic Behavior. Princeton University Press.

149.                 von Neumann, J., & Morgenstern, O. (1944). Theory of Games and Economic Behavior. Princeton University Press.

150.                 Wallerstein, J. S., & Lewis, J. M. (2004). The Unexpected Legacy of Divorce: Report of a 25-Year Study. Psychoanalytic Psychology, 21(3), 353-370.

151.                 Walt, S. M. (1987). The Origins of Alliances. Cornell University Press.

152.                 Waltz, K. N. (1979). Theory of International Politics. Addison-Wesley.

153.                 Washburn, A., & Kress, M. (2009). Combat Modeling. Springer.

154.                 Wasserman, S., & Faust, K. (1994). Social Network Analysis: Methods and Applications. Cambridge University Press.

155.                 Wilson, E. O. (1975). Sociobiology: The New Synthesis. Harvard University Press.

156.                 Wong, L., Bliese, P., & McGurk, D. (2003). Military Leadership: A Context Specific Review. The Leadership Quarterly, 14(6), 657-692.

157.                 Woolley, A. W., Chabris, C. F., Pentland, A., Hashmi, N., & Malone, T. W. (2010). Evidence for a Collective Intelligence Factor in the Performance of Human Groups. Science, 330(6004), 686-688.

Yarosh, S., Chew, Y. C., & Abowd, G. D. (2009). Supporting Parent-Child Communication in Divorced Families. International Journal of Human-Computer Studies, 67(2), 192-203.

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