Understanding Market Dynamics

An actuarial perspective on the role of social networks and suppositions in financial markets

Bryon Robidoux February 2025

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Photo: Getty Images/shutter2photos

As actuaries, we interact with and model financial markets. One fundamental task is understanding how people form expectations, which I explored in my article “Expectations, Probability and Uncertainty.” It explained how expectations are mathematical constructs based on probability theory, but suppositions are uncertain beliefs about the future based on neuroscience. A point being that expectations and suppositions are nowhere near equivalent.

Now, I want to move beyond how individuals form suppositions and look at how social networks form them, which is one of the bases for market prices. I believe it is critical for actuaries to understand how social dynamics impact market prices because it will allow them to understand where their models may be insufficient because of market behavior.

Defining the Problem Due to Risk’s Assumptions

Famed economist Fredrich Hayek stated in his work “The Use of Knowledge in Society” in the American Economic Review in 1945: “I do not deny that equilibrium analysis is useful in our system. But it does not deal with the social process at all, and it is no more than a useful preliminary to the study of the main problem.”¹

Let’s look at problems regarding issues with risk metrics.

Explicit Unattainable Knowledge

Hayek expressed: “I fear that our theoretical habits of approaching the problem with the assumption of more or less perfect knowledge on the part of almost everyone has made us somewhat blind to the true function of the price mechanism and led us to apply rather misleading standards in judging its efficiency.”²

Risk theory is derived directly from equilibrium analysis. It explicitly assumes that people know all future outcomes with certainty, which eventually leads to proving market efficiency.³ This knowledge is impossible unless you are in a casino setting—where all the games have static rules, so outcomes are fixed and known. The only uncertainty is the outcome that will occur.

Risk’s knowledge assumption exceeds people’s memory constraints. It derives directly from the set theory because sets have fixed membership. The fixed membership forces the event space to stay static and certain in the future. People do not live in a risky but uncertain world because they can’t know all future outcomes.⁴ When creating assumptions, we must replace ‘as if’ with ‘as is.’⁵

Reliance on Knowledge Concentration

As Hayek further illuminated: “[In] the practical [economic] problem … we must show how a solution is produced by the interactions of people, each of whom possesses only partial knowledge.”⁶

Risk uses correlation, a linear measure, but market interactions are almost certainly not linear. Hence, risk mostly ignores interactions in a risk paradigm. Risk and expectation theory assumption focuses knowledge on a single source capable of managing market activities, but this is not realistically possible or desirable. Having a single entity orchestrating all activities would be brittle, time-consuming and impossible to compute. Furthermore, centralized control cannot feasibly adapt to change. The system would get completely overwhelmed, the opposite of always being in stable equilibrium.

Designing the Solution Based on Cognitive Science

Hayek again stated the solution to understanding market dynamics: “In ordinary language, we describe by the word ‘planning’ the complex, interrelated decisions about allocating our available resources. The various ways in which the knowledge on which people base their plans is communicated to them is the crucial problem for any theory explaining the economic process. The problem of what is the best way of utilizing knowledge initially dispersed among all the people is at least one of the main problems of economic policy—or of designing an efficient economic system.”⁷

Let’s explore how distributed knowledge coordinates to form market prices.

Understanding Prices

Uncertainty requires that event spaces change as living systems adapt, evolve and innovate, creating new possible outcomes. Therefore, people and the market do not form expectations but suppositions because expectations require a static event space. Suppositions are uncertain beliefs about the future based on current and past knowledge and experience. Prices are the supposition of future availability, need and desire for a particular commodity or service.

Let’s build the mechanics of supposition from the ground up, starting with the brain, which is the source of our knowledge.

Environment Sampling

The brain creates brain maps to store information. Brain maps are abstractions that represent knowledge. The synapses form the relationships among the abstractions, combining the abstractions in different ways to create more elaborate abstractions.⁸

The Neuroscience of You states that our brains have similar structures and chemistry, but they have slight variations, which leads to our brains working differently. This difference changes the frequencies we sample in our external environment and internal brain maps. These sampling rates dictate the rate at which we receive environmental feedback relative to our internal maps’ perception.⁹ Therefore, the sampling rates shape how we perceive the world around us, which creates and modifies our brain maps and their relationships.

Why Distributed Knowledge?

Steven Sloman and Philip Fernbach explain in their book, The Knowledge Illusion: Why We Never Think Alone, our propensity to collect our peers’ expertise and observe our environment to increase our computational capacity. The book’s central thesis, as I understand it, is that we do this so naturally that we do not even realize it until we are questioned explicitly about our knowledge. We are often overconfident in the portion that we contribute. This overconfidence happens because, even though it feels as if our understanding is unbounded, we only know approximately five gigabytes of information.¹⁰

Using each other’s expertise and our environment dramatically helps us improve our computational capacity while staying within our brains’ energy and space constraints. What makes us exceptional is not our individual computational abilities, I assert, but how we significantly magnify our intelligence by working together. Let’s dig into why teamwork is so powerful.

Building Relationships to Optimize Distributed Knowledge

We naturally seek out people with sampling rates like ours, which is analogous to tuning the strings of a guitar. This tuning process involves mirroring our close peers from a selfish viewpoint so we can modify our brain maps to match their behavior better. We do this naturally because it increases our ability to guess others’ behavior and refine the relationship to our behavior.¹¹

Accurate forecasts reward our brains with dopamine hits.¹² These rewards give us a sense of belonging and safety, which naturally causes us to assemble in like-minded, i.e., homophilic, herds.¹³ The like-minded packs seed strong ties within our peer networks. Hence, suppositions form through continual feedback loop sampling between the homophilic networks’ knowledge and the individuals. Therefore, the peer network’s suppositions help form the individuals’ suppositions and vice versa. This process helps improve the accuracy of the group’s supposition by reducing the cognitive dissonance in the individual suppositions, which puts us in tune. It is our natural error-correcting process.

Homophily

Homophily, derived from the Greek, means “the love of sameness.” It is so fundamental to human networks, and we are so familiar with it, that it fades into the background.¹⁴ The brain uses abstractions and their relationships to minimize energy and space constraints. Homophily reduces the energy and time constraints of humans’ collective information processing. But how?

Reconciling Robot Networks to Human Networks

Imagine a group of robots that must work together to perform a task. If all the robots are direct copies of each other, meaning they contain the same programming and data, then packets of information sent between them can be minimal and task oriented. Now imagine that you start making the robots more and more dissimilar; robots must send more information to accomplish the same task. Information storage, transformation and interpretation will take considerable time and energy as they become more heterogeneous. Slow communication would destroy computational efficiency.¹⁵ Therefore, heterogeneity kills the advantage of solving problems with multiple robots. What gives?

Now imagine the number of robots growing from eight to 8 billion. Even though they would all be different, similarities in communication would emerge. Then engineers could create transmission control protocols/internet protocols (TCP/IP), i.e., rules and conventions, to allow robots that function similarly to communicate more efficiently. In humans, culture, customs and language are what TCP/IP are to robots.

Therefore, homophily gives rise to a plethora of cultures around the world. These cultural rules create informational mechanisms that allow for easy forecasts of messages and behavior. Without these informational mechanisms, large language models like ChatGPT could not exist. Therefore, homophily—coupled with culture—increases communication speed by reducing signal noise and reducing energy for human computing outcomes.

Brazilian neuroscientist Suzana Herculano-Houzel explains that we are the only animals that cook our food, which allows us to extract far more calories from the food to feed our brains. If this were not the case, we would starve due to the energy requirements of our brains. As we evolved to cook, as stated by Herculano-Houzel, “We rapidly developed culture, agriculture, civilization, grocery stores, electricity and refrigerators to get food even faster.”¹⁶

What we consider human evolution should be more accurately rebranded, I believe, as the evolution of the social computational strategies for achieving energy, communication and computational efficiency. Computation begets ever more efficient computation.

Putting It All Together

Risk theory suggests that nature solves problems from a reductionist linear or top-down approach focused on the objective. This is contradicted in Kenneth Stanley and Joel Lehman’s book, Why Greatness Cannot Be Planned: The Myth of the Objective. In it, they explain that nature searches for the most dissimilar, novel solutions, which counterintuitively leads to faster convergence to a solution.¹⁷ Hence, nature solves problems from an emergent, nonlinear, bottom-up approach. The emergence happens, as I understand it, by taking many tiny similar agents, such as humans, ants or whatever, and running a massively parallel computation with a decentralized control structure.¹⁸

How Does Nature Converge to a Supposition With Distributed Knowledge?

Based on knowledge, each agent produces a novel, dissimilar solution to the problem by being flung out to saturate the event space. The agents use the composition and concentration of signals to coordinate their efforts and converge on their target.¹⁹ The signals (targets) can be pheromones (food) in ants, dances (nectar) for bees or language (suppositions) for humans.²⁰ The more substantial the signal, the more positive feedback is received, and the faster and more intense they will converge upon the target.

The key concept is that differences in our thinking create random variations in the process, increasing convergence speed. Even though we all think differently, we have an evolutionarily refined automatic framework for efficient, low-energy computation.

Market Stability and Instability

The low-energy computation feedback loop within like-minded groups leads to the market’s perceived stability and efficiency, as I see it. As new individuals receive information, they share it with their groups. When a new infection or idea arrives within a strongly connected, like-minded network, the redundancy between the links creates immunity to new ideas and contagion through the negative feedback from existing ideas.²¹ This immunity is a source of market stability. The stronger the relationships between members of the group, the more stable the group’s behavior. Through this process, robust liquid markets will inadvertently collude—due to information immunity—to keep prices relatively stable. The volatility of a stable market is the friction of ideas sloshing around.

The tipping point is that the tightly-knit group switches its position on a topic because a new idea has infected a significant portion of the group’s population. The tipping point can be as low as 25%, much lower than the 50% intuition would lead you to believe.²² A market downturn or significant upswing happens when the markets hit their tipping point. The infection from new information spreads quickly through the market participants, which prompts the agents to find the new target. They move in a new direction only when enough participants become infected with counterevidence. As I see them, the financial markets are human beehives.

Bottom Line

Hayek stated that one of the main problems of economic policy is understanding the utilization of knowledge dispersed among all people, which is what I hope this article has demonstrated.²³ This point of view contradicts how we calculate market prices based on expectation, but it aligns nicely with how market prices form through the market’s supposition.

I believe risk has been so ingrained in our culture that we never stop and question its foundation or conclusions. We never possess perfect knowledge with certainty. One could be well-served understanding that our social behavior derives from maximizing computation, given our brains’ energy, space, time and memory constraints—all while dealing with imperfect, incomplete and uncertain outcomes. Any viable theory and model should account for this.

We have a sampling rate corresponding to our peers, so we can align with them to speed up computation, minimize memory constraints and reduce sampling errors. We have a dopamine reward system to ensure that this happens.

It is not enough to huddle into like-minded groups. We also must be able to communicate efficiently with people within the group. Like-minded groups create a culture that acts as the communication protocol and provides predictable behaviors to increase understanding and computational speed at minimal energy levels. As we evolve, this has become even more refined and efficient.

When you put this all together, you get a bunch of heterogeneous individuals searching the landscape for novel possibilities in a parallel fashion, like bees looking for nectar. The individuals bring back their suppositions to the group. The group quickly learns the best possibilities based on the communicated information, which is the perceived stability and efficiency in the market. However, as this process continues, people will bring new ideas and information to influence the group. Only when there is a large enough consensus will the group move to a new, better set of possibilities, which results in significant market downturns or upswings. These tipping points, as I would call them, are explicitly assumed to not exist in a risk paradigm.

Statements of fact and opinions expressed herein are those of the individual authors and are not necessarily those of the Society of Actuaries or the respective authors’ employers.

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