The fall of an apple, an elevator, a market: Seeking natural forces in human economy
Legend has it that only when an apple fell on Isaac Newton’s head, did he at last perceive the force of gravity. Only after his famous thought experiment of a man in a falling elevator, was Albert Einstein able to unify that same force of gravity into his theory of general relativity. Quotidien occurrences (which hopefully do not include falling elevators), observed from a novel perspective, was all it took to launch some of the greatest advancements in science — more specifically, observing falls from a different perspective.
Despite innumerous drops, plunges, and crashes, the natural force that drives the world’s markets and economies’ rises and falls remains undiscovered, if not ignored, or even completely refuted — at the very least, resolved as an ethereal “invisible hand.” As a result of this persistent ignorance, we continue to fall without knowing why, and until we uncover this unknown force, we will continue surprising ourselves each time. As with apples and elevators, however, the answer may have been under our noses all along.
There are countless theories that seek to explain the concept of an “economy.” This diversity, though, suggests that either there are various “economies” or we — as humans — do not yet understand what an economy truly is.
I must admit that I too struggle to define economics, having studied it for a decade and lived it for a couple more. One of the first definitions I learned — maybe the most common in introductory courses in the United States — is that economics is the system of allocating scarce resources. The economy refers to how we distribute and use what is available to us in both the physical and intellectual realms to achieve a certain objective. That goal may be profit, happiness, or mere survival.
Survival, of course, is not a purely economic goal, let alone human one; it is a fundamental end of all biological entities. Thus, if every life’s primary objective is survival, could it be that this very concept of economics, of using scarce resources to survive, is as strictly human as we tend to treat it?
These questions have fell on my head early on in my study of economics, and other the years, I have looked for new perspectives to answer some of what can often seem the most basic.
Economic self-symmetry and superorganic behavior
All around us, we can find countless nonhuman species behaving economically, in ways that we can economically analyze. One such biological system, which presents particularly similar patterns to human economies, is that of the humble ant.
Ants, like us humans, are social animals; they naturally form groups, “colonies.” Simply observing a colony, we quickly appreciate the complex system that arises from discrete interactions among thousands, millions of individuals. In science, such systems can be referred to as interaction networks, of which our human society is just one example.
Scientists categorize an ant colony as a “superorganism,” which in general means that a colony can appear as a single biological entity, being born, consuming, growing, moving, and even reproducing. Seen in this light, a colony can begin to appear a replica of the very ants which compromise it. Mathematics refers to this replication that traverses scales and magnitudes as “self-similarity,” and it is a phenomenon profoundly present in our lives — just look at a plant, a cloud, a wave, or a mountain to see so. In broad terms, ants’ interaction networks within the colony result in the collective’s superorganic behavior.
Over time, I have noticed phenomena of human economy that — viewed through this same prism of self-similarity and superorganisms — we might consider instances of such superorganic behavior.
Firstly, an economy clearly interacts with and is influenced by the environment in which it exists. In very broad strokes, the weather affects economic activity, and economic activity produces impacts on the environment. Secondly, economies physically consume and grow. Economists have already demonstrated the ability to predict economic results by analyzing satellite images of the earth at night, making projections based simply on the intensity of light in different areas of the globe — illuminating economies’ both consumption of energy and expansion. And finally, the spread of different economic systems — such as a particular nation’s, capitalism, and/or communism — can begin appearing like superorganic reproduction and, of course, competition.
In other words, perhaps, we can consider an economy as a human superorganism.
Information: The fabric of economic nets
Thus, if we were to view ourselves as ants (apologies for the pessimistic, yet cliché, analogy) and our economy as the colony, then perhaps, we can model our behavior — as well as that of our superorganic economy — based on biological and/or zoological phenomena.
Myrmecologists can model ants’ behavior with surprisingly simple algorithms. For example, the task an individual ant chooses at any given moment is determined by a logic defined by the rates of said ant’s interaction with its fellow colony members and of the information[i] transferred in each interaction. As the biologist and myrmecology researcher Deborah Gordon explains the ‘thought process’ of an ant resembles something like:
“‘If I meet another ant with odor A [ants communicate through scent] about three times in the next 30 seconds, the probability that I will go out to forage will increase by about 10%; if not, it will go down by about 20%.’”
It is, of course, impossible to read an ant’s mind; nevertheless, to understand the dynamics of and make predictions about a colony, we need only know the individual ants’ task at any given time. Extrapolating an ant’s role and/or objective as it relates to the current needs of the greater colony, in fact, requires just two easily observable variables: task and state (i.e. active or inactive).
If we stop and actually watch a chain of ants, we see that the chain has a bidirectional flow. On a regular basis, ants going in opposite directions will stop and ‘converse’ (by odor) briefly before either both continuing on or one turning around and following the other. We will have watched them exchange information and making — from our perspective — a binary decision: continue or turn around. For all we know, though, they will have exchanged pleasantries, complained about other colony members, or anything else we can imagine, but ultimately, that potential complexity is irrelevant to the actions of the interaction network. From our perspective and that of the colony’s, in these interactions, each ant is merely a vessel, a unit of information, a single bit: forward or backward, TRUE or FALSE, 1 or 0.
We know first-hand that human beings’ forms of communication and interaction networks are overwhelmingly complex; however, we must recognize that when we see two ants interacting, we can only observe the final result of the information exchange; we cannot know what they actually “discussed.” Similarly, despite our own ever-so-complex communication, what matters in the end from the perspective of macroeconomic activity and/or our greater interaction network, is also the final result of information we receive in communication — our action/reaction.
The stock market is one such example and, perhaps, one of the most volatile as well — subject to crashes and sudden jumps in billions or even trillions of dollars in minutes, if not seconds. And, these rises and falls not only happen more often than we like to admit, but also seem to obey much less economic logic than we would like to think.
Of course, as behavioral economics and finance have come to show, any individual investor acts in ways far from economically rational; however, taken as a whole, humanity tends to make decisions that appear much more logical and/or empirically correct: the so-called wisdom of crowds. In reality, though, the crowd constantly gets it wrong as well especially in the case of major stock market swings.
How, then, can we have crowds (i.e. interaction networks) that can demonstrate both wisdom and frantic irrationality? In the case of the ant, it is influenced by the information it receives in the interaction network’s chain of communication as well as by the rate at which it receives information in general, such as in Dr. Gordon’s example algorithm above. Based on this information and the rate of reception, the ant either changes its present state or not.
A given individual trader on the stock market, in the end, appears to act in a similar way when viewed from afar. They decide whether to change their state (e.g. buy or sell versus hold) based on the information they are receiving and the rate at which information comes in — be it news, earnings calls, or even just watching the stock price’s graph. What researchers have observed, however, is that during stock market climbs or falls, investor decisions begin to increasingly mirror each other, thus detaching stock prices from their original business and economic fundamental. This decision-making mirroring produces greater co-movement of stock prices (the ratio of stocks increasing or decreasing together at the same time), and a rise in co-movement is an unusually accurate leading indicator of market crashes. The theory here is that greater co-movement — which correlates with volatility — reflects individuals’ increasingly basing decisions on and/or mirroring others’ actions, as opposed to basing decisions on economic fundamentals (information that they receive in less volume and at lower rates).[ii]
Either way, in this case, could mapping the flow of information and identifying how individuals make decisions accordingly produce an upscalable macroeconomic model? If so, there are several machine learning techniques that can produce networks composed of nodes programmed with similar algorithms.
Getting from micro to macro and back again
This example of the stock market also stresses the gap that exists between microeconomic and macroeconomic modelling — that is, understanding how individual decisions collectively produce macroeconomic trends. Currently, micro and macro seem to exist in two different modelling universes.
Nevertheless, it seems most economic theories on either scale apply a simple algebraic logic to the way economic results arise. Behavioral economics has already poked many holes in such assumptions about economic rationality, but these phenomena have mainly challenged microeconomic thought.
There is less discussion about the possibilities that macroeconomics might not adhere to models that assume any factors beyond money supply, price level, government spending, and taxation rates can simply fit into a black box labelled Z₁. Of course, other models have evolved to uncover the functions within that black box, but again they seem to derive connections with purely “economic” factors. For example, Farmer’s IS-LM-NAC model purports that a market’s animal spirits result from functions such as xₜ=Eₜ (pₖ,ₜ₊₁/pₜ₊₁) and pₖ,ₜ = βrrₜ/(1-β), where represents how markets’ beliefs are a function of stock prices, pₖ,ₜ₊₁, which are in turn a function of dividend, rrₜ, and discount rates, β.
Models must extract the driving factors from the noise and, as such, may inherently appear simple. But, a model that derives an entire market’s economic expectation — that is, a human collective’s decision-making process — from dividend and discount rates is, in my humble opinion, just answering one question with another. I am still left asking: how do individual humans determine their discount rates, and how does that manifest at the macroeconomic level?
So many great discoveries of natural scientific laws appear to occur when the scientist stops ignoring what is inconvenient and instead sees it as the manifestation of something more fundamental. An example I always think of is that of Boltzmann and his formula for predicting the entropy of gases, S=klnW. Frustrated for years trying to extract predictions by thinking about the movement of each atom, his eureka moment arrived when he zoomed out to a larger scale and treated the gas — the collective of atoms — as a single entity (I’ll resist the urge to draw a parallel here to a superorganism).
I wonder if our understanding of economics is currently trapped by a similar fixation on the incorrect scale. That is not to say that analyzing the macroeconomy at a macro scale is wrong but that we must stop stuffing inconvenient factors into a black box and begin applying a different lens to this superorganism’s behavior.
In fact, there is already experimental evidence that hints at forces more fundamental in the macroeconomy. For example, neuroscientists have already demonstrated the ability to neuroforecast economic decisions. When asked to which crowdfunding loan an individual would contribute money, researchers can predict answers based on activity in the medial prefrontal cortex. Predicting the outcomes of crowdfunding loan campaigns amongst groups of individuals, or aggregate choice, however, becomes much more accurate when rather than assess individuals’ medial prefrontal cortex activity, their nucleus accumbens activity is analyzed instead.
So, while executive function may correlate more closely to microeconomic decisions, at the macro scale, our reptilian brains may offer better indicators — perhaps “animal spirits” really was the appropriate term.
Now what?
At this point, we are already reaching the frontiers of scientific knowledge about individual and collective human decision-making. While there is much to be elaborated mathematically about concepts such as the few I’ve mentioned here, the fact is that these are all still very much open questions — especially in the context of economics. Although I much prefer to bring solutions rather than problems, the search for fundamental, natural economic forces requires more minds asking questions and seeking answers.
As it so happens, at the time of this writing (May 2020), we are once again experiencing a sudden fall; perhaps, the apple has once again fallen on our heads. Rather than simply suffer the (economic) pain, this time, let us ask not “why?” but “how?” instead.
We must stop seeing our economic system as separate from the greater natural system in which it exists, in which we human beings exist. Because, for as long as we continue letting the apple land on our heads, we will continue understanding the cause only as much as our animal brethren. Understanding the invisible hand requires imagination, faith. Understanding the force that pushes us forward, however, will require, above all else, science.
[i] The term “information” has gained many new definitions in recent decades. In this piece, by “information,” I mean it in the most technical sense, from that of Information Theory, which essentially boils down to the resolution of uncertainty.
[ii] I should note that the modern-day stock is driven in large part by computer-based automated and/or algorithmic trading. At first glance, I realize this might seem to imply that markets are not the manifestation of human behavior, and that is fair, to a point. The fact is, though, that the nodes of an interaction network need not all be the same species (let alone genus, kingdom, or even alive); what matters is the transmission of and reactions to information. Further, humans demonstrated long ago that they did not need robots to induce stock market crashes and mania.
