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Question

The Productive Value Bridge Between Behavioral and Traditional Economics

Lance Amundsen

Abstract

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    1
Abstract    1
Postulates    3
Productive Value and Productive Power    3
Information Both is and Describes Productive Value    5
Categories of Productive Power    5
Measuring Productive Value Exchanges    6
Willing Interactions versus Unwilling Interactions    8
Profit versus Loss and Risk    9
Evaluating Productive Power    10
Magnifying Productive Value    11
Technology    12
Teamwork    12
Natural Selection and Survival of the Fittest    12
The Productive Power of Societies    13
The Importance of Societal Defense    14
Why Some Demographics Fall Economically Behind Others    14
The Bridge to Behavioral Economics    15
The Bridge to Traditional Economics    16
Summary    17
Additional Topics for Examination    18
Applicability to Species other than Humans    18
On Well Being and Mental Health    19
Investigating and Quantifying Non-typical Productive Power Group Categories    20
Examining the Productive Power of Past Competing Societies    21
Further Mathematical Investigation    23
Further Analysis of Information within the Model    24
Separated Parent Families    25
Analysis of Militaries    26
References    28

 
Postulates
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
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).


<paper continues.... too long for this post... message me for the full draft>

Comments

Would you be willing to email me the full draft? I don't have the credentials to offer a holistic review, but I would love to read the rest out of interest. Thanks.

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Absolutely.  My email is LCA13@yahoo.com.  Drop me an email address to send it to.

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I also posted a few more pages under a different post:

https://www.aeaweb.org/forum/4506/using-chatgpt-ai-for-publishing

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Last thing: the ChatGPT learning algorithm picked up on my post.  You can now ask ChatGPT about my work.  Here is it's opinion base on the little I have posted on this forum:

From ChatGPT:
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The significance of Lance Amundsen's **Productive Value Model** in economics depends on several factors, including its originality, applicability, and the extent to which it addresses gaps in existing theories.

### Strengths:
1. **Interdisciplinary Approach**: The model's attempt to bridge **behavioral economics** and **traditional economic theories** could provide fresh insights, especially if it effectively incorporates intangible forms of value like skills, knowledge, and interpersonal interactions.
2. **Comprehensive Framework**: By introducing concepts like **Productive Value (PV)** and **Productive Power (PP)**, it seems to offer a broader lens to study economic exchanges, moving beyond monetary transactions to include intangible assets.

### Challenges:
1. **Empirical Validation**: Without empirical studies or mathematical formalization, its claims remain theoretical. The model needs rigorous testing to establish relevance and accuracy.
2. **Academic Acceptance**: Economics as a discipline often prioritizes models that are testable and applicable across contexts. Gaining traction in peer-reviewed journals or conferences is crucial for recognition.
3. **Overlap with Existing Theories**: The concepts of value exchange and productivity have been explored extensively in other frameworks. The model must clearly differentiate itself to be significant.

### My Perspective:
If the **Productive Value Model** introduces genuinely novel ways to measure and understand value exchanges, it could be a valuable addition to economics. However, its current lack of peer-reviewed backing or widespread discussion suggests it has not yet demonstrated such potential.

To assess its significance, further development, formalization, and application to real-world problems are needed. If these steps prove successful, it could open up interesting new dimensions in economic analysis.

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More insight of the model from ChatGPT:
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Lance Amundsen's **Productive Value Model** appears to have interesting conceptual elements that merit exploration, even if it is currently untested. Let’s break it down:

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### **Conceptual Strengths**
1. **Broadening the Definition of Value**:
   - The model emphasizes **Productive Value (PV)**, which includes not just material goods and services but also intangible assets like knowledge and skills.
   - This aligns with modern trends in economics, where the value of information, intellectual property, and human capital is increasingly critical.

2. **Dynamic Nature of Value Creation**:
   - By introducing **Productive Power (PP)**—the ability to generate PV—the model recognizes that individuals' economic potential evolves through education, experience, and skill acquisition. This dynamic perspective is refreshing.

3. **Behavioral and Traditional Economics Integration**:
   - Combining behavioral insights (e.g., subjective valuations of exchanges) with traditional economics could provide a richer understanding of real-world interactions.

4. **Interpersonal Exchange Focus**:
   - Highlighting the exchange of intangible values (e.g., trust, expertise, and reputation) could fill a gap in classical economic models, which often focus narrowly on monetary transactions.

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### **Does It Make Sense?**
Yes, **conceptually it makes sense**—but its utility depends on how well it addresses the following challenges:

1. **Measurement**:
   - How do you quantify Productive Value (PV) and Productive Power (PP)? For instance, measuring the "value" of knowledge or trust is notoriously tricky.
   - If these concepts cannot be operationalized, their theoretical usefulness diminishes.

2. **Relevance**:
   - Many aspects of the model—such as valuing skills and knowledge—overlap with existing economic theories (e.g., human capital theory or social capital concepts). The model needs to clearly differentiate itself.

3. **Scalability**:
   - Does the model work at macroeconomic levels (e.g., nations, industries), or is it limited to interpersonal exchanges? Broader applicability would enhance its relevance.

4. **Validation**:
   - Economic theories thrive or falter based on their ability to predict or explain real-world phenomena. Without empirical testing, it’s impossible to say how well the model holds up.

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### **Intellectual Potential**
At its core, the **Productive Value Model** makes sense as an intuitive way to frame economic interactions, especially those involving non-material exchanges. Its potential lies in its ability to:
- Provide fresh metrics for analyzing value beyond money.
- Offer insights into how individual potential impacts economic systems.
- Address gaps in how we model the "intangibles" that dominate modern economies (e.g., digital goods, reputation systems, and knowledge-based industries).

However, the key to its significance will be moving beyond abstraction and demonstrating applicability. If it can clarify or solve real-world economic problems—particularly those poorly addressed by existing models—it could be groundbreaking. Until then, it remains a promising but speculative idea.

Answer 1

Author Update — May 2026: From the Productive Value Model to the PV-PP Framework

This original post contains a partial early draft of work that has since developed substantially. I am leaving it in place because it reflects the origin of the project: the intuition that individuals and systems exchange forms of value beyond money alone, that information matters both as something conveyed and as something that shapes later action, and that productive capacity changes over time through interaction.

However, readers should not treat this early Productive Value Model draft as the current formulation of the theory. The developed work is now referred to as our Productive Value–Productive Power (PV-PP) framework, and it is no longer best described simply as a proposed bridge between behavioral economics and traditional economics. It has become a broader, non-scalar, multi-domain decision and governance architecture concerned with state, perception, viability, constraints, recovery, execution, and the conditions under which actions remain permissible or productive in context.

Several early formulations should therefore be qualified or retired as statements of current doctrine.

First, Productive Power (PP) is no longer treated as a broad additive “net worth” measure into which every capability, asset, piece of information, or outcome should be translated. In the current framework, PP is a structured state made up of domains. Those domains may be continuous, discrete, symbolic, or Boolean; they are not assumed to be commensurable; and they need not support arithmetic aggregation. Safety, health, evidence adequacy, authority, recoverability, knowledge, capital, and other relevant domains may matter differently in different applications. Some can be compared or scored in restricted settings. Others may operate as thresholds or non-tradeable conditions.

Second, Productive Value (PV) is no longer adequately described as merely “everything exchanged” or as something that simply adds to or subtracts from PP. PV is better understood as structured input that can modify a persistence-bearing system’s state through a transition process. An interaction that appears beneficial on one dimension may degrade another domain, cross a viability threshold, remove recovery capacity, or create downstream risk. For that reason, earlier shorthand statements such as “positive PV increases PP” should be read as introductory intuition, not as the present formal rule.

Third, the developed framework distinguishes actual Productive Power from Perceived Productive Power (PPP). An individual or an agent does not generally act from perfect knowledge of its actual state or environment. It acts from a perceived state shaped by information, interpretation, memory, uncertainty, and sometimes error or deception. Information matters, but it does not automatically modify actual PP merely because it was received. It changes perceived state first; actual state changes through later selected and executed actions. This distinction has become important in applications involving decision error, recovery, behavioral distortion, and AI agents.

Fourth, the framework is explicitly non-scalar. The early draft spoke in places as though the project might eventually quantify all PV in a common monetary measure. That is not the current position. A central reason for developing PV-PP is that some decision environments cannot be understood adequately by forcing all relevant considerations into one compensatory score. A missing authorization, a failed safety condition, stale evidence, loss of recoverability, or collapse of a governing domain is not always a “cost” that can be offset by enough gain elsewhere.

This does not mean scalar reasoning is rejected. The current scalar-reduction research program takes a more disciplined position: scalar comparison can be valid inside our PV-PP framework as a restricted, certifiable subroutine when the structural conditions support it. In a narrowly defined containment subclass, scalar comparison is internally recoverable and reaches the same result as the PV-PP selection architecture. But that local containment result is not a claim that all scalar models have been contained, nor is it a claim that scalar methods are generally invalid.

The complementary claim is also bounded. In specified classes of cases, scalar comparison does not faithfully reproduce the full PV-PP architecture without collapsing stage structure or importing the very governing information the scalar formulation was supposed to replace. For example, a scalar method may reproduce a final output in a static case while failing to preserve the distinction between governing-domain screening and later permitted refinement. In dynamic cases, if the relevant governing state changes while a state-independent scalar sees the same fixed consequence inputs, a fixed scalar ranking may no longer recover the changed PV-PP output. These are conditional non-reduction results, not universal impossibility claims against every conceivable scalar, nonlinear, or vector-valued representation.

The present public-facing formulation is therefore narrower, but stronger: scalar comparison is useful where certified; it is not assumed to be the complete architecture of decision or governance.

The framework has also moved beyond its initial economic motivation into practical applications. One current application is AI agent governance. In that setting, the relevant question is often not simply whether an agent has access to a tool, possesses prior authorization, or achieves a favorable performance score. The question is whether a proposed action is actually viable in the current state: whether the evidence is current, authority exists, constraints are satisfied, escalation is available, recovery remains possible, and execution would stay inside the governing boundary.

That application also illustrates why the developed framework is declarative. Real governance is usually already distributed across policy documents, operating procedures, thresholds, permissions, workflow logic, code, configuration, logs, and execution paths. Our PV-PP work aims to represent those conditions directly as governance objects rather than hide them inside one score or an expanding stack of procedural exceptions. The current PV-PP Agent Auditor is an advisory tool intended to surface governance gaps. Work on pre-execution viability control and runtime enforcement remains under development and should not be confused with a finished enforcement product.

Finally, the early post should not be read as having resolved empirical validation, formal completeness, or academic acceptance. Since the original draft, the work has advanced through formal architecture documents, operator specifications, benchmark simulations, scalar containment and non-reduction studies, memory/perception work, and agent-governance applications. But the project remains active research. Some branches are mature enough to state carefully; others remain exploratory or open.

The historical idea in this post still matters: value and productive capacity cannot be understood only through monetary exchange, and information can materially shape what individuals and systems are able to do. The developed PV-PP framework now expresses that idea more carefully: as a structured, non-scalar, viability-oriented architecture for analyzing state, perception, interaction, choice, recovery, and execution.

Current research materials, public demos, and project updates are available at:
https://amundsenlance.github.io