Despite its success in financial markets and other domains, collective intelligence seems to fall short in many critical contexts, including infrequent but repeated financial crises, political polarization and deadlock, and various forms of bias and discrimination. We propose an evolutionary framework that provides fundamental insights into the role of heterogeneity
and feedback loops in contributing to failures of collective intelligence. The framework is based on a binary choice model of behavior that affects fitness; hence, behavior is shaped by evolutionary dynamics and stochastic changes in environmental conditions. We derive collective intelligence as an emergent property of evolution in this framework, and also specify conditions under which it fails. We find that political polarization emerges in stochastic environments with reproductive risks that are correlated across individuals. Bias and discrimination emerge when individuals incorrectly attribute random adverse events to observable features that may have nothing to do with those events. In addition, path dependence and negative feedback in evolution may lead to even stronger biases and levels of discrimination, which are locally evolutionarily
stable strategies. These results suggest potential policy interventions to prevent such failures by nudging the “madness of mobs” towards the “wisdom of crowds” through targeted shifts in the environment

Hamilton’s rule [W. D. Hamilton, Am. Nat. 97, 354–356 (1963); W. D. Hamilton,
J. Theor. Biol. 7, 17–52 (1964)] quantifies the central evolutionary ideas of inclusive fitness and kin selection into a simple algebraic relationship. Evidence consistent with Hamilton’s rule is found in many animal species. A drawback of investigating Hamilton’s rule in these species is that one can estimate whether a given behavior is consistent with the rule, but a direct examination of the exact cutoff for altruistic behavior predicted by Hamilton is almost impossible. However, to the degree that economic resources confer survival benefits in modern society, Hamilton’s rule may be applicable to economic decision-making, in which case techniques from experimental economics
offer a way to determine this cutoff. We employ these techniques to examine whether Hamilton’s rule holds in human decision-making, by measuring the dependence between an experimental subject’s maximal willingness to pay for a gift of $50 to be given to someone else and the genetic relatedness of the subject to the gift’s recipient. We find good agreement with the predictions of Hamilton’s rule. Moreover, regression analysis of the willingness to pay versus genetic relatedness, the number of years living in the same residence, age, and sex shows that almost all the variation is explained by genetic relatedness. Similar but weaker results are obtained from hypothetical questions regarding the maximal risk to

We study the relationships between the real-time psychophysiological activity of professional traders, their financial transactions, and market fluctuations. We collected multiple physiological signals such as heart rate, blood volume pulse, and electrodermal activity of 55 traders at a leading global financial institution during their normal working hours over a nfive-day period. Using their physiological measurements, we implemented a novel metric of
trader’s “psychophysiological activation” to capture affect such as excitement, stress and irritation. We find statistically significant relations between traders’ psychophysiological activation levels and such as their financial transactions, market fluctuations, the type of financial products they traded, and their trading experience. We conducted post-measurement interviews with traders who participated in this study to obtain additional insights in the key
factors driving their psychophysiological activation during financial risk processing. Our work illustrates that psychophysiological activation plays a prominent role in financial risk processing for professional traders.

Bayes' rule is a fundamental principle that has been applied across multiple disciplines. However, few studies have addressed its origin as a cognitive strategy or the underlying basis for generalization from a small sample. Using a simple binary choice model subject to natural selection, we derive Bayesian inference as an adaptive behavior under certain stochastic environments. Such behavior emerges purely through the forces of evolution, despite the fact that our population consists of mindless individuals without any ability to reason, act strategically, or accurately encode or infer environmental states probabilistically. In addition, three specific environments favor the emergence of finite memory—those that are Markov, nonstationary, and environments where sampling contains too little or too much information about local conditions. These results provide an explanation for several known phenomena in human cognition, including deviations from the optimal Bayesian strategy and finite memory beyond resource constraints.

One of the longest debates in economics involves the existence of a rare Hominid “species” known as Homo economicus, the economic human. H. economicus is able to determine the optimal use of its resources to maximize its well-being as defined by the assumptions of neoclassical economics, leading to behavior that has come to be known as economic rationality. When interacting with other members of this species in market settings, such behavior leads to a magical outcome. The participants’ self-interested efforts to exploit their disparate pieces of information aggregates, distills, and compresses their information into a single number: the price. And because no piece of information is left unused or uninterpreted in the process of price discovery, this market is deemed “efficient.” Prices fully reflect all available information, as Eugene Fama concluded in his first articulation of the efficient markets hypothesis (1).

We construct an evolutionary model of a population consisting of two types of interacting individuals that reproduce under random environmental conditions. We show that not only does the evolutionarily dominant behavior maximize the number of offspring of each type, it also minimizes the correlation between the number of offspring of each type, driving it toward −1. We provide several examples that illustrate how correlation can be used to explain the evolution of cooperation.

We use a global survey of over 22,400 individual investors, 4,892 financial advisors, and 2,060 institutional investors between 2015 and 2017 to elicit their asset allocation behavior and risk preferences. We find substantially different behaviors among these three groups of market participants. Most institutional investors exhibit highly contrarian reactions to past returns in their equity allocations. Financial advisors are also mostly contrarian; a few of them demonstrate passive behavior. However, individual investors tend to extrapolate past performance. We use a clustering algorithm to partition individuals into five distinct types: passive investors, risk avoiders, extrapolators, contrarians, and optimistic investors. Across demographic categories, older investors tend to be more passive and risk averse.

One of the most enduring empirical regularities in equity markets is the inverse relationship between stock prices and volatility. Also known as the “leverage effect”, this relationship was first documented by Black (1976), who attributed it to the effects of financial or operating leverage. This paper documents that firms which had no debt (and thus no financial leverage) from January 1973 to December 2017 exhibit Black’s leverage effect. Moreover, it finds that the leverage effect of firms in this sample is not driven by operating leverage. On the contrary, in this sample the leverage effect is stronger for firms with low operating leverage as compared to those with high operating leverage. Interestingly, the firms with no debt from the lowest quintile of operating leverage exhibit the leverage effect that is on par with or stronger than that of debt-financed firms.

This two-volume set brings together a unique collection of key publications at the intersection of biology and economics, two disciplines that share a common subject: Homo sapiens. Beginning with Thomas Malthus–whose dire predictions of mass starvation due to population growth influenced Charles Darwin–economists have routinely used biological arguments in their models and methods. This collection summarizes the most important of these developments, including articles in sociobiology, evolutionary psychology, behavioral ecology, behavioral economics and finance, neuroeconomics, and behavioral genomics. Together with an original introduction by the editors, this important research collection will appeal to economists, biologists, and practitioners looking to develop a deeper understanding of the limits of Homo Economicus.

The information revolution has transformed everyday life for billions of people throughout the world. For example, according to mobile phone research group GSMA Intelligence, there are currently over 5 billion unique mobile phone subscribers, out of an estimated global population of 7.6 billion. This is the equivalent of a mobile phone for every person on the planet between the ages of 15 and 65.

Bacteria are easily characterizable model organisms with an impressively complicated set of abilities. Among them is quorum sensing, a cell-cell signaling system that may have a common evolutionary origin with eukaryotic cell-cell signaling. The two systems are behaviorally similar, but quorum sensing in bacteria is more easily studied in depth than cell-cell signaling in eukaryotes. Because of this comparative ease of study, bacterial dynamics are also more suited to direct interpretation than eukaryotic dynamics, e.g., those of the neuron. Here we review literature on neuron-like qualities of bacterial colonies and biofilms, including ion-based and hormonal signaling, and a phenomenon similar to the graded action potential. This suggests that bacteria could be used to help create more accurate and detailed biological models in neuroscientific research. More speculatively, bacterial systems may be considered an analog for neurons in biologically based computational research, allowing models to better harness the tremendous ability of biological organisms to process information and make decisions.

Half of all Americans have money in the stock market, yet economists can't agree on whether investors and markets are rational and efficient, as modern financial theory assumes, or irrational and inefficient, as behavioral economists believe—and as financial bubbles, crashes, and crises suggest. This is one of the biggest debates in economics and the value or futility of investment management and financial regulation hang on the outcome. In this groundbreaking book, Andrew Lo cuts through this debate with a new framework, the Adaptive Markets Hypothesis, in which rationality and irrationality coexist.

Drawing on psychology, evolutionary biology, neuroscience, artificial intelligence, and other fields, Adaptive Markets shows that the theory of market efficiency isn't wrong but merely incomplete. When markets are unstable, investors react instinctively, creating inefficiencies for others to exploit. Lo's new paradigm explains how evolution shapes behavior and markets at the speed of thought—a fact revealed by swings between stability and crisis, profit and loss, and innovation and regulation.

A fascinating intellectual journey filled with compelling stories, Adaptive Markets starts with the origins of market efficiency and its failures, turns to the foundations of investor behavior, and concludes with practical implications—including how hedge funds have become the Galápagos Islands of finance, what really happened in the 2008 meltdown, and how we might avoid future crises.