Tomás Lejarraga

How choice ecology influences search in decisions from experience

Research into human decision-making has often sidestepped the question of search despite its importance across a wide range of domains such as search for food, mates, allies, visual targets or information. Recently, research on decisions from experience has made progress in finding out how individual characteristics shape search for information. Surprisingly little is known, however, about how the properties of the choice ecology shape peoples search. To fill this void, we analyzed how two key ecological properties influence search effort: domain of choice (gains vs. losses) and experienced variance (variance vs. no variance). Many people search longer when facing the prospect of losses relative to gains. Moreover, most people search more in options in which they experience variance relative to options they experience as invariant. We conclude that two factors that have been identified as important determinants of choice also influence search of information.

A Loser Can Be a Winner: Comparison of Two Instance-based Learning Models in a Market Entry Competition

This paper presents a case of parsimony and generalization in model comparisons. We submitted two versions of the same cognitive model to the Market Entry Competition (MEC), which involved four-person and two-alternative (enter or stay out) games. Our model was designed according to the Instance-Based Learning Theory (IBLT). The two versions of the model assumed the same cognitive principles of decision making and learning in the MEC. The only difference between the two models was the assumption of homogeneity among the four participants: one model assumed homogeneous participants (IBL-same) while the other model assumed heterogeneous participants (IBL-different). The IBL-same model involved three free parameters in total while the IBL-different involved 12 free parameters, i.e., three free parameters for each of the four participants. The IBL-different model outperformed the IBL-same model in the competition, but after exposing the models to a more challenging generalization test (the Technion Prediction Tournament), the IBL-same model outperformed the IBL-different model. Thus, a loser can be a winner depending on the generalization conditions used to compare models. We describe the models and the process by which we reach these conclusions.

The Two Settings of Kind and Wicked Learning Environments

Inference involves two settings: In the first, information is acquired (learning); in the second, it is applied (predictions or choices). Kind learning environments involve close matches between the informational elements in the two settings and are a necessary condition for accurate inferences. Wicked learning environments involve mismatches. This conceptual framework facilitates identifying sources of inferential errors and can be used, among other things, to suggest how to target corrective procedures. For example, structuring learning environments to be kind improves probabilistic judgments. Potentially, it could also enable economic agents to exhibit maximizing behavior.

Decisions from Experience: How Groups and Individuals Adapt to Change

Whether groups make better judgments and decisions than individuals has been studied extensively, but most of this research has focused on static tasks. How do groups and individuals compare in settings where the decision environment changes unexpectedly and without notification? This article examines group and individual behavior in decisions from experience where the underlying probabilities change after some trials. Consistent with the previous literature, the results showed that groups performed better than the average individual while the decision task was stable. However, group performance was no longer superior after a change in the decision environment. Group performance was closer to the benchmark of Bayesian updating, which assumed perfect memory. Findings suggest that groups did not adopt decision routines that might have delayed their adaption to change in the environment. Rather, they seem to have coordinated their responses, which led them to behave as if they had better memory and subsequently delayed adaptation.

How the threat of losses makes people explore more than the promise of gains

Until recently, loss aversion has been inferred exclusively from choice asymmetries in the loss and gain domains. This study examines the impact of the prospect of losses on exploratory search in a situation in which exploration is costly. Taking advantage of the largest available data set of decisions from experience, analyses showed that most people explore payoff distributions more under the threat of a loss than under the promise of a gain. This behavioral regularity thus occurs in both costly search and cost-free search (see Lejarraga, Hertwig, & Gonzalez, Cognition, 124, 334–342, 2012). Furthermore, a model comparison identified the simple win-stay-lose-shift heuristic as a likely candidate mechanism behind the loss–gain exploration asymmetry observed. In contrast, models assuming loss aversion failed to reproduce the asymmetry. Moreover, the asymmetry was not simply a precursor of loss aversion but a phenomenon separate from it. These findings are consistent with the recently proposed notion of intensified vigilance in the face of potential losses.

Description and experience: How experimental investors learn about booms and busts affects their financial risk taking.

A few years ago, the world experienced the most severe economic crisis since the Great Depression. According to the depression baby hypothesis, people who live through such macroeconomic shocks take less financial risk in their future lives (e.g., lower stock market participation). This hypothesis has previously been tested against survey data. Here, we tested it in a simulated experimental stock market (based on the Spanish stock index, IBEX-35), varying both the length of historical data available to participants (including or excluding a macroeconomic shock) and the mode of learning about macroeconomic events (through sequential experience or symbolic descriptions). Investors who learned about the market from personal experience took less financial risk than did those who learned from graphs, thus echoing the description-experience gap observed in risky choice. In a second experiment, we reversed the market, turning the crisis into a boom. The description-experience gap persisted, with investors who experienced the boom taking more risk than those who did not. The results of a third experiment suggest that the observed gap is not driven by a wealth effect, and modeling suggests that the description-experience gap is explained by the fact that participants who learn from experience are more risk averse after a negative shock. Our findings highlight the crucial role of the mode of learning for financial risk taking and, by extension, in the legally required provision of financial advice.

When Experience Meets Description: How Dyads Integrate Experiential and Descriptive Information in Risky Decisions

How do teams make joint decisions under risk when some team members learn about a prospect from description and others learn from experience? In a series of experiments, we find that two-person teams composed of one participant who learns from description and a second participant who learns from experience arrive at shared decisions via mutual concessions. In doing so, they attenuate individual biases, such as the overweighting and underweighting of the probability of rare events. The social interaction thus leads dyads to make shared decisions that follow normative standards more closely than the decisions made by individual decision makers. Finally, in processing experiential information, dyads appear to be sensitive to the reliability of the experience: the more reliable the experiential information, the larger its influence on the dyad’s decision. Data, as supplemental material, are available at http://dx.doi.org/10.1287/mnsc.2016.2428. This paper was accepted by Teck-Hua Ho, judgment and decision making.

Context-specific, scenario-based risk scales.

Reacting to an emergency requires quick decisions under stressful and dynamic conditions. To react effectively, responders need to know the right actions to take given the risks posed by the emergency. While existing research on risk scales focuses primarily on decision making in static environments with known risks, these scales may be inappropriate for conditions where the decision makers time and mental resources are limited and may be infeasible if the actual risk probabilities are unknown. In this article, we propose a method to develop context-specific, scenario-based risk scales designed for emergency response training. Emergency scenarios are used as scale points, reducing our dependence on known probabilities; these are drawn from the targeted emergency context, reducing the mental resources required to interpret the scale. The scale is developed by asking trainers/trainees to rank order a range of risk scenarios and then aggregating these orderings using a Kemeny ranking. We propose measures to assess this aggregated scales internal consistency, reliability, and validity, and we discuss how to use the scale effectively. We demonstrate our process by developing a risk scale for subsurface coal mine emergencies and test the reliability of the scale by repeating the process, with some methodological variations, several months later.

Experience and description: Exploring two paths to knowledge

Experience and description are powerful ways of learning and adaptation. Recently, evidence has shown that these can imply systematically distinct cognitions and behaviors. However, there has been little integrative conceptual work. Drawing on different lines of research, we characterize experience and description, sketch the factors that influence learning from them, and suggest how to reconcile previously disparate research. We propose that much can be gained by studying the behavioral, cognitive, and hedonic implications of description- and experience-based learning in parallel.

The pyeTribe: Simultaneous eyetracking for economic games

The recent introduction of inexpensive eyetrackers has opened up a wealth of opportunities for researchers to study attention in interactive tasks. No software package has previously been available to help researchers exploit those opportunities. We created “the pyeTribe,” a software package that offers, among others, the following features: first, a communication platform between many eyetrackers to allow for simultaneous recording of multiple participants; second, the simultaneous calibration of multiple eyetrackers without the experimenter’s supervision; third, data collection restricted to periods of interest, thus reducing the volume of data and easing analysis. We used a standard economic game (the public goods game) to examine the data quality and demonstrate the potential of our software package. Moreover, we conducted a modeling analysis, which illustrates how combining process and behavioral data can improve models of human decision-making behavior in social situations. Our software is open source.