Behavioral Biases and the Law

Empirical and experimental evidence shows that individuals exhibit behavioral biases in their decision-making processes that depart from the full rationality paradigm. This paper discusses the effectiveness of alternative debiasing strategies, designed to induce socially preferable outcomes. Following Jolls, C. and Sunstein, C.R. (2006). Debiasing through law. J. Leg. Stud. 35: 199–242, this paper examines legal strategies that aim at “debiasing through law”, attempting to reduce or eliminate boundedly rational behavior. Alternatively, policymakers can implement “insulating” legal strategies that separate the outcome from the biased behavior, without attempting to eradicate behavioral biases from the decision-making process. This paper compares these strategies in many areas, such as tort law, consumer safety law, and property law.

Inferred Switching Behavior in Response to Re-entry of Uber and Lyft: A Revealed Study in Austin, TX

In May 2016, Uber and Lyft suspended services following the defeat of Proposition 1 in Austin, TX. In response, several ridesourcing services entered the market. This paper provides information on the individuals most likely to switch back to Uber or Lyft after service restoration using estimated indifference bands. We hypothesize that people will not switch back unless a given trip’s cost saving is beyond a positive threshold due to boundedly rational behavior. The study revealed, among others, that those who use TNC for social purposes, have lower indifference bands and are more likely to switch to Uber or Lyft.

Bounded Rationality in Principal-Agent Relationships

We conducted six treatments of a standard moral hazard experiment with hidden action. The behavior in all treatments and periods was inconsistent with established agency theory. In the early periods, behavior differed significantly between treatments. This difference largely vanished in the final periods. We used logit agent quantal response equilibrium (LAQRE) as a device to grasp boundedly rational behavior and found the following: (1) LAQRE predictions are much closer to subjects’ behavior in the laboratory; (2) LAQRE probabilities and experimental behavior show remarkably similar patterns; and (3) including social preferences in LAQRE does not better explain the experimental data; (4) LAQRE cannot explain the contract offers of some players who seem to choose some focal contract parameters.

THE E-MAIL GAME REVISITED — MODELING ROUGH INDUCTIVE REASONING

I study the robustness of Rubinstein's (1989) E-Mail Game results by varying the information that players can utilize. The article follows one of Morris' (2002) reactions to the E-Mail game "that one should try to come up with a model of boundedly rational behavior that delivers predictions that are insensitive to whether there is common knowledge or a large number of levels of knowledge". Players in my model are presumed to use 'rough inductive reasoning' because they cannot utilize exact information. The information structure in the E-Mail game is generalized and the conditions are characterized under which Rubinstein's results hold. I find that rough inductive reasoning generates a payoff dominant equilibrium where the expected payoffs change continuously (instead of discretely) in the probability of "faulty" communication.

Strategic Errors and Audit Quality: An Experimental Investigation

We report experimental results for a simple bimatrix game between a manager and an auditor. We investigate whether the quality of audited financial statements improves if the relative proportion of perfectly honest auditors increases. We find that the quality of audited reports is higher if computerized auditors who always perform a high audit effort are present. This result is in direct contrast to the Nash prediction for our game. The Quantal Response Equilibrium, which explicitly takes into account boundedly rational behavior, supports our experimental data. Our main findings depend neither on framing nor the payoff matrix we use.

Incentives for Boundedly Rational Agents

This paper develops a theoretical framework for analyzing incentive schemes under bounded rationality. It starts from a standard principal-agent model and then superimposes an assumption of boundedly rational behavior on the part of the agent. Boundedly rational behavior is modeled as an explicit optimization procedure, which combines gradient dynamics with imitation and experimentation. The results predict the underprovision of optimal incentives and deviation from a standard sufficient statistics result from the agency literature. It also allows us to address the question of creating the optimal incentives in a multicultural environment.

Bounded Rationality and Materials Selection

The vulnerabilities inherent in choosing an innovative material for a design can make for technological catastrophe. The points of weakness are not dependent on the technical nature of the innovation, but instead are intrinsic to human problem-solving and decision-making. We present a computational approach for managing and containing this fallibility using ideas from cognitive science and artificial intelligence, in particular, understanding how the boundedly rational behavior of humans and organizations leads to specific kinds of errors that affect the choice and use of materials.Materials choice in an industrial setting is often the result of a long chain or network of circumstances whose origins lie in many domains—from traditional engineering practice to the very latest innovation, from the commercial to the technical. In turn, every materials-selection decision has a long chain of consequences that are difficult to compute. This lengthy chain of consequences of a single decision gives rise to numerous points of error. What is particularly troublesome about these errors is not that they arise because of incomplete scientific engineering knowledge, but rather that they occur in spite of the fact that all (or at least, almost all) of the technically relevant information is available and sometimes even known to the technical personnel on the project. We will illustrate this approach by using a historical example of a major materials failure. Advances in database-systems design present an opportunity for integrating the ontology of material attributes with properties data. This may enable the design of more appropriate validation procedures required in proving a material for an artifact.