Exploratory ventures outside the established disciplinary boundaries can yield added insights and explanatory power. Imposing cognitive limitations on human logical reasoning ability (bounded rationality) is a well-known case in point. Extending cognition to parts of body outside the brain, and to environment outside the body is another. In contrast, the present article takes a constructive approach, also in an exploratory spirit. For the sake of exposition, we consider three tiered realms of scientific inquiry: physical or inanimate, biological or animate, and socio-psychological or sentient. In this three-tier framework, we explore the extent of gains in modeling human action within the confines of physical principles such as optimization. In this exercise, concepts of complexity and emergence account for the absence of analytically derivable mapping from micro or finer grain phenomena to macro or coarser grain phenomena. A general notion of embodiment captures the inclusion of a more expansive range of explanatory factors in modeling and understanding a given phenomenon. Emergence and embodiment play complementary roles in exploration of human behavior.
We propose framing human action in physics before reaching to biology and social sciences, rearranging the order of their usual deployment. As an example, consider efforts to model altruism that start in a frame of psychological or social attributes such as reciprocity, empathy, and identity. Evolutionary roots might also be used by appeal to survival of the species from biology. Only then the modeler abstracts to work on notations, and to establish relationships using mathematical apparatus from physics. This top-down deployment of principles from various scientific disciplines has generated a body of coherent models, partially generalizable theories, and disagreements. In this paper we present a definition of action as a movement between two points in the relevant space, and explore reversing the direction of deploying scientific theories, starting with the principle of least action in physics to frame observed human action. Used as an organizing principle of the whole universe, optimization element in human behavior does not have to be presumed to arise from animate aspects of adaptive and cognitive faculties; emergence of social phenomena, when optimal, can be disconnected from methodological individualism. Our three-tier framework makes room for physical, biological and social science principles, proposing a new perspective on human behavior, sans reductionism.
To explore how speculative trading influences prices in financial markets, we conduct a laboratory market experiment with speculating investors (who do not collect dividends and trade only for capital gains) and dividend-collecting investors. Moreover, we operate markets at two different levels of money supply. We find that in phases with only speculating investors present (i) price deviations from fundamentals are larger; (ii) prices are more volatile; (iii) mispricing increases with the number of transfers until maturity; and (iv) speculative trading pushes prices upward (downward) when the supply of money is high (low). These results suggest that controlling the money supply can help to stabilize asset prices.
Forming beliefs or expectations about others’ behavior is fundamental to strategy, as it co-determines the outcomes of interactions in and across organizations. In the game theoretic conception of rationality, agents reason iteratively about each other to form expectations about behavior. According to prior scholarship, actual strategists fall short of this ideal, and attempts to understand the underlying cognitive processes of forming expectations about others are in their infancy. We propose that emotions help regulate iterative reasoning, that is, their tendency to not only reflect on what others think, but also on what others think about their thinking. Drawing on a controlled experiment, we find that a negative emotion (fear) deepens the tendency to engage in iterative reasoning, compared to a positive emotion (amusement). Moreover, neutral emotions yield even deeper levels of iterative reasoning. We tentatively interpret these early findings and speculate about the broader link of emotions and expectations in the context of strategic management. Extending the view of emotional regulation as a capability, emotions may be building blocks of rational heuristics for strategic interaction and enable interactive decision-making when strategists have little experience with the environment.
Forming beliefs or expectations about others’ behavior is fundamental to strategy, as it co-determines the outcomes of interactions in and across organizations. In the game theoretic conception of rationality, agents reason iteratively about each other to form expectations about behavior. According to prior scholarship, actual strategists fall short of this ideal, and attempts to understand the underlying cognitive processes of forming expectations about others are in their infancy. We propose that emotions help regulate iterative reasoning, that is, their tendency to not only reflect on what others think, but also on what others think about their thinking. Drawing on a controlled experiment, we find that a negative emotion (fear) deepens the tendency to engage in iterative reasoning, compared to a positive emotion (amusement). Moreover, neutral emotions yield even deeper levels of reasoning. We tentatively interpret these early findings and speculate about the broader link of emotions and expectations in the context of strategic management. Extending the view of emotional regulation as a capability, emotions may be building blocks of rational heuristics for strategic interaction and enable interactive decision-making when strategists have little experience with the environment.
Information dissemination and aggregation are key economic functions of financial markets. How intelligent do traders have to be for the complex task of aggregating diverse information (i.e., approximate the predictions of the rational expectations equilibrium) in a competitive double auction market? An apparent ex-ante answer is: intelligent enough to perform the bootstrap operation necessary for the task—to somehow arrive at prices that are needed to generate those very prices. Constructing a path to such equilibrium through rational behavior has remained beyond what we know of human cognitive abilities. Yet, laboratory experiments report that profit motivated human traders are able to aggregate information in some, but not all, market environments (Plott and Sunder 1988, Forsythe and Lundholm 1990). Algorithmic agents have the potential to yield insights into how simple individual behavior may perform this complex market function as an emergent phenomenon. We report on a computational experiment with markets populated by algorithmic traders who follow cognitively simple heuristics humans are known to use. These markets, too, converge to rational expectations equilibria in environments in which human markets converge, albeit slowly and noisily. The results suggest that high level of individual intelligence or rationality is not necessary for efficient outcomes to emerge at the market level; the structure of the market itself is a source of rationality observed in the outcomes.
Social sciences start by looking at the social-psychological attributes of humans to model and explain their observed behavior. However, we suggest starting the study of observed human behavior with the universal laws of physics, e.g., the principle of minimum action. In our proposed three-tier framework, behavior is a manifestation of action driven by physical, biological, and social-psychological principles at the core, intermediate, and top tier, respectively. More broadly, this reordering is an initial step towards building a platform for reorganizing the research methods used for theorizing and modeling behavior. This perspective outlines and illustrates how a physical law can account for observed human behavior and sketches the elements of a broader agenda.
To explore how speculative trading influences prices in financial markets, we conduct a laboratory market experiment with speculating investors (who do not collect dividends and trade only for capital gains) and dividend-collecting investors. Moreover, we operate markets at two different levels of money supply. We find that in phases with only speculating investors present (i) price deviations from fundamentals are larger; (ii) prices are more volatile; (iii) mispricing increases with the number of transfers until maturity; and (iv) speculative trading pushes prices upward (downward) when the supply of money is high (low). These results suggest that controlling the money supply can help to stabilize asset prices.
We use a laboratory experiment to compare general equilibrium economies in which agents individually allocate their private goods among consumption, investment in production. and replenishing or refurbishing a depreciating public facility in a dynamic game with long-term investment opportunities. The public facility is financed either by voluntary anonymous contributions (VAC) or taxes. We find that rates of taxation chosen by majority vote remain at an intermediate level close to the finite-horizon optimum, and the experimental economies sustain public goods at levels between the finite- and infinite-horizon optima. This contrasts with a rapid decline of public goods under VAC. Both the payoff efficiency and production of private goods are higher when taxes are set endogenously instead of being fixed at the infinite-horizon optimum level externally. When taxes are adjusted to the respective finite-horizon optimum each period, production levels and efficiency remain as high as in the voting treatments at least in the latter half of the sessions. When subjects choose between VAC and taxation, 23 out of 24 majority votes favor taxation, demonstrating a clear preference for enforceable taxes to finance public goods in this setting.
Nudge and boost are two competing approaches to applying the psychology of reasoning and decision making to improve policy. Whereas nudges rely on manipulation of choice architecture to steer people towards better choices, the objective of boosts is to develop good decision-making competences. Proponents of both approaches claim capacity to enhance social welfare through better individual decisions. We suggest that such efforts should involve a more careful analysis of how individual and social welfare are related in the policy context. First, individual rationality is not always sufficient or necessary for improving collective outcomes. Second, collective outcomes of complex social interactions among individuals are largely ignored by the focus of both nudge and boost on individual decisions. We suggest that the design of mechanisms and social norms can sometimes lead to better collective outcomes than nudge and boost, and present conditions under which the three approaches (nudge, boost, and design) can be expected to enhance social welfare.
What are the properties of a good financial reporting regime? There are three broad approaches to defining better financial reporting based on attributes, goals, and practice. The first specifies some attributes of good reporting. A second approach is to focus on goals of society or of some specified individuals or groups. Looking to practice for guidance on defining and understanding the financial reporting regime is the third major approach. These three approaches – attributes, goals, and practice – are not mutually exclusive. It is unlikely that any one of them is entirely satisfactory by itself; they complement one another. Better Financial Reporting argues for such a syncretic attitude to financial reporting regime.
Attainment of rational expectations equilibria in asset markets calls for the price system to disseminate agents’ private information to others.Markets populated by human agents are known to be capable of converging to rational expectations equilibria. This paper reports comparable market outcomes when human agents are replaced by boundedly-rational algorithmic agents who use a simple means-end heuristic. These algorithmic agents lack the capability to optimize; yet outcomes of markets populated by them converge near the equilibrium derived from optimization assumptions. These findings point to market structure (rather than cognition or optimization) being an important determinant of efficient aggregate level outcomes.
The possibility of the presence of multiple equilibria in closed exchange and production-and-exchange economies is usually ignored in macroeconomic models even though they are important in real economies. We argue that default and bankruptcy laws serve to provide the conditions for uniqueness of an equilibrium. In this paper, we report experimental evidence on the effectiveness of this approach to resolving multiplicity: a society can assign default penalties on fiat money so that the economy selects one of the equilibria. The laboratory data show that the choice of default penalty takes the economy near the chosen equilibrium. The theory and evidence together reinforce the idea that accounting, bankruptcy and possibly other aspects of social mechanisms play an important role in resolving the otherwise mathematically intractable challenges associated with multiplicity of equilibria in closed economies.
Economics, long thought beyond the reach of experimentation, began gradually to yield some of its inaccessible secrets to laboratory and field investigations over the past century. Observation and analyses of laboratory games have led economists to think about markets as social artifacts, whether evolved or designed, to achieve predictable outcomes in specified environments. The value of potential insights into properties of macro economic models and policies had to overcome the barrier of virtual impossibility of conducting controlled experiments at macro economic scale. Fortunately, micro models of macro phenomena have allowed experiments to identify the more plausible from sets of multiple or indeterminate outcomes, and assess policy alternatives and institutional designs. Without attempting a comprehensive survey, this paper summarizes some important discoveries from experimental economics, with special emphasis on macro economics. Challenges ahead are mentioned briefly.