Contradictory deviations from maximization: Environment-specific biases, or reflections of basic properties of human learning?

Analyses of human reaction to economic incentives reveal contradictory deviations from maximization. For example, underinvestment in the stock market suggests risk aversion, but insufficient diversification of financial assets suggests risk-seeking. Leading explanations for these contradictions assume that different choice environments (e.g., different framings) trigger different biases. Our analysis shows that variation in the choice environment is not a necessary condition. It demonstrates how certain changes in the incentive structure are sufficient to trigger six pairs of contradictory deviations from maximization even when the choice environment is fixed. Moreover, our analysis shows that the direction of these deviations can be captured by assuming that choice propensities reflect reliance on small samples of past experiences. In order to clarify the underlying processes, we considered distinct models of the reliance on small samples assumption, and compared them to classical models of choice (including prospect theory). The comparison focused on both within-individual, and between-group predictions (based on a preregistered study with 120 new tasks). The results reveal large advantage of "wide sampling" models that (in the static settings we examine) approximate an effort to rely on the most similar past experiences. Surprisingly, we also found that assuming that the parameters reflect stable individual traits impairs predictions; it seems that the number of "most similar past experiences" for each individual varies from task to task. These results suggest that ignoring the predictable impact of the incentive structure can lead to exaggeration of the importance of environment- and individual-specific decision biases. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Motivational drivers for serial position effects: Evidence from high-stakes legal decisions.

Experts and employees in many domains make multiple similar but independent decisions in sequence. Often, the serial position of the case in the sequence influences the decision. Explanations for these serial position effects focus on the role of decision-makers' fatigue, but these effects emerge also when fatigue is unlikely. Here, we suggest that serial position effects can emerge due to decision-makers' motivation to be or appear consistent. For example, to avoid having inconsistencies revealed, decisions may become more favorable toward the side that is more likely to put a decision under scrutiny. As a context, we focus on the legal domain in which many high-stakes decisions are made in sequence and in which there are clear institutional processes of decision scrutiny. We analyze two field data sets: 386,109 U.S. immigration judges' decisions on asylum requests and 20,796 jury decisions in 18th century London criminal court. We distinguish between five mechanisms that can drive serial position effects and examine their predictions in these settings. We find that consistent with motivation-based explanations of serial position effects, but inconsistent with fatigue-based explanations, decisions become more lenient as a function of serial position, and the effect persists over breaks. We further find, as is predicted by motivational accounts, that the leniency effect is stronger among more experienced decision-makers. By elucidating the different drivers of serial position effects, our investigation clarifies why they are common, when they are expected, and how to reduce them. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Frequency of enforcement is more important than the severity of punishment in reducing violation behaviors.

External enforcement policies aimed to reduce violations differ on two key components: the probability of inspection and the severity of the punishment. Different lines of research offer different insights regarding the relative importance of each component. In four studies, students and Prolific crowdsourcing participants (Ntotal = 816) repeatedly faced temptations to commit violations under two enforcement policies. Controlling for expected value, we found that a policy combining a high probability of inspection with a low severity of fines (HILS) was more effective than an economically equivalent policy that combined a low probability of inspection with a high severity of fines (LIHS). The advantage of prioritizing inspection frequency over punishment severity (HILS over LIHS) was greater for participants who, in the absence of enforcement, started out with a higher violation rate. Consistent with studies of decisions from experience, frequent enforcement with small fines was more effective than rare severe fines even when we announced the severity of the fine in advance to boost deterrence. In addition, in line with the phenomenon of underweighting of rare events, the effect was stronger when the probability of inspection was rarer (as in most real-life inspection probabilities) and was eliminated under moderate inspection probabilities. We thus recommend that policymakers looking to effectively reduce recurring violations among noncriminal populations should consider increasing inspection rates rather than punishment severity.

To predict human choice, consider the context.

Choice prediction competitions suggest that popular models of choice, including prospect theory, have low predictive accuracy. Peterson et al. show the key problem lies in assuming each alternative is evaluated in isolation, independently of the context. This observation demonstrates how a focus on predictions can promote understanding of cognitive processes.

On the Value of Alert Systems and Gentle Rule Enforcement in Addressing Pandemics.

The COVID-19 pandemic poses a major challenge to policy makers on how to encourage compliance to social distancing and personal protection rules. This paper compares the effectiveness of two policies that aim to increase the frequency of responsible health behavior using smartphone-tracking applications. The first involves enhanced alert capabilities, which remove social externalities and protect the users from others' reckless behavior. The second adds a rule enforcement mechanism that reduces the users' benefit from reckless behavior. Both strategies should be effective if agents are expected-value maximizers, risk averse, and behave in accordance with cumulative prospect theory (Tversky and Kahneman, 1992) or in accordance with the Cognitive Hierarchy model (Camerer et al., 2004). A multi-player trust-game experiment was designed to compare the effectiveness of the two policies. The results reveal a substantial advantage to the enforcement application, even one with occasional misses. The enhanced-alert strategy was completely ineffective. The findings align with the small samples hypothesis, suggesting that decision makers tend to select the options that lead to the best payoff in a small sample of similar past experiences. In the current context, the tendency to rely on a small sample appears to be more consequential than other deviations from rational choice.

Perceived patterns in decisions from experience and their influence on choice variability and policy diversification: A response to Ashby, Konstantinidis, & Yechiam, 2017.

Searching for and acting upon perceived patterns of regularity is a fundamental learning process critical for adapting to changes in the environment. Yet in more artificial, static settings, in which patterns do not exist, this mechanism could interfere with choice maximization and manifest as unexplained choice variability in later trials. Recently however, Ashby et al. (2017) found that choice variability in later trials of a repeated choice setting is correlated with levels of diversification in policy tasks, in which patterns can never be exploited. They concluded that in repeated choice tasks, choice-variability in later trials is unlikely the result of following perceived patterns. Here, we demonstrate that correlations between choice variability and policy diversification can actually be the result of pattern seeking, rather than serving as evidence against it. We review evidence for the robustness of pattern seeking mechanisms in repeated choices and explain how such mechanisms could in fact create the results observed by Ashby et al. To examine our interpretation for their results, we conducted a sequential dependencies analysis of their data and find evidence that many participants behaved as if they believed trials are inter-dependent, even though they were explicitly instructed that the environment is stationary. The results of a new experiment in which sequential patterns are directly manipulated support our interpretation: Experiencing patterns affected both choice variability in later trials and policy diversification. Finally, we argue that decisions from experience tasks are a valid tool to examine the generation of preferences via fundamental learning processes.

From anomalies to forecasts: Toward a descriptive model of decisions under risk, under ambiguity, and from experience.

Experimental studies of choice behavior document distinct, and sometimes contradictory, deviations from maximization. For example, people tend to overweight rare events in 1-shot decisions under risk, and to exhibit the opposite bias when they rely on past experience. The common explanations of these results assume that the contradicting anomalies reflect situation-specific processes that involve the weighting of subjective values and the use of simple heuristics. The current article analyzes 14 choice anomalies that have been described by different models, including the Allais, St. Petersburg, and Ellsberg paradoxes, and the reflection effect. Next, it uses a choice prediction competition methodology to clarify the interaction between the different anomalies. It focuses on decisions under risk (known payoff distributions) and under ambiguity (unknown probabilities), with and without feedback concerning the outcomes of past choices. The results demonstrate that it is not necessary to assume situation-specific processes. The distinct anomalies can be captured by assuming high sensitivity to the expected return and 4 additional tendencies: pessimism, bias toward equal weighting, sensitivity to payoff sign, and an effort to minimize the probability of immediate regret. Importantly, feedback increases sensitivity to probability of regret. Simple abstractions of these assumptions, variants of the model Best Estimate and Sampling Tools (BEAST), allow surprisingly accurate ex ante predictions of behavior. Unlike the popular models, BEAST does not assume subjective weighting functions or cognitive shortcuts. Rather, it assumes the use of sampling tools and reliance on small samples, in addition to the estimation of the expected values. (PsycINFO Database Record

Learning in settings with partial feedback and the wavy recency effect of rare events.

Analyses of human learning reveal a discrepancy between the long- and the short-term effects of outcomes on subsequent choice. The long-term effect is simple: favorable outcomes increase the choice rate of an alternative whereas unfavorable outcomes decrease it. The short-term effects are more complex. Favorable outcomes can decrease the choice rate of the best option. This pattern violates the positive recency assumption that underlies the popular models of learning. The current research tries to clarify the implications of these results. Analysis of wide sets of learning experiments shows that rare positive outcomes have a wavy recency effect. The probability of risky choice after a successful outcome from risk-taking at trial t is initially (at t+1) relatively high, falls to a minimum at t+2, then increases for about 15 trials, and then decreases again. Rare negative outcomes trigger a wavy reaction when the feedback is complete, but not under partial feedback. The difference between the effects of rare positive and rare negative outcomes and between full and partial feedback settings can be described as a reflection of an interaction of an effort to discover patterns with two other features of human learning: surprise-triggers-change and the hot stove effect. A similarity-based descriptive model is shown to capture well all these interacting phenomena. In addition, the model outperforms the leading models in capturing the outcomes of data used in the 2010 Technion Prediction Tournament.

Reliance on small samples, the wavy recency effect, and similarity-based learning.

Many behavioral phenomena, including underweighting of rare events and probability matching, can be the product of a tendency to rely on small samples of experiences. Why would small samples be used, and which experiences are likely to be included in these samples? Previous studies suggest that a cognitively efficient reliance on the most recent experiences can be very effective. We explore a very different and more cognitively demanding process explaining the tendency to rely on small samples: exploitation of environmental regularities. The first part of our study shows that across wide classes of dynamic binary choice environments, focusing only on experiences that followed the same sequence of outcomes preceding the current task is more effective than focusing on the most recent experiences. The second part of our study examines the psychological significance of these sequence-based rules. It shows that these tractable rules reproduce well-known indications of sensitivity to sequences and predict a nontrivial wavy recency effect of rare events. Analysis of published data supports this wavy recency prediction, but suggests an even wavier effect than these sequence-based rules predict. This pattern, and the main behavioral phenomena documented in basic decisions from experience and probability learning tasks, can be captured with a similarity-based model assuming that people follow sequences of outcomes most of the time but sometimes respond to trends. We conclude with theoretical notes on similarity-based learning.