Selected Publications
(in chronological order)
Plonsky, O., Teodorescu, K., & Erev, I. (2015). Reliance on Small Samples, the Wavy Recency Effect, and Similarity-based Learning. Psychological Review, 122(4), 621–647. doi: 10.1037/a0039413
Click to expand abstract
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.
Download pdf. Direct link.
Plonsky, O., & Erev, I. (2017). Learning in Settings with Partial Feedback and the Wavy Recency Effect of Rare Events. Cognitive Psychology, 93, 18-43. doi: 10.1016/j.cogpsych.2017.01.00
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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 out-comes 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 out-comes have a wavy recency effect. The probability of risky choice after a successful out-come 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.
Download pdf. Direct link.
Erev, I., Ert, E., Plonsky, O., Cohen D., & Cohen O. (2017). From Anomalies to Forecasts: Toward a Descriptive Model of Decisions under Risk, under Ambiguity, and from Experience. Psychological Review, 124(4), 369-409. doi: 10.1037/rev0000062
Click to expand abstract
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.
Download pdf. Direct link. Competition’s website. Competition’s data.
Plonsky, O., Erev, I., Hazan, T., & Tennenholtz, M. (2017). Psychological Forest: Predicting Human Behavior. In The Proceedings of the Thirty-first AAAI Conference on Artificial Intelligence (AAAI-17), 656-662.
Click to expand abstract
We introduce a synergetic approach incorporating psychological theories and data science in service of predicting human behavior. Our method harnesses psychological theories to extract rigorous features to a data science algorithm. We demonstrate that this approach can be extremely powerful in a fundamental human choice setting. In particular, a random forest algorithm that makes use of psychological features that we derive, dubbed psychological forest, leads to prediction that significantly outperforms best practices in a choice prediction competition. Our results also suggest that this integrative approach is vital for data science tools to perform reasonably well on the data. Finally, we discuss how social scientists can learn from using this approach and conclude that integrating social and data science practices is a highly fruitful path for future research of human behavior.
Download pdf. Direct link.
Plonsky, O., & Teodorescu, K. (2020). The Influence of Biased Exposure to Forgone Outcomes. Journal of Behavioral Decision Making, 33, 393– 407. doi: 10.1002/bdm.2168
Click to expand abstract
After making decisions, we often get feedback concerning forgone outcomes (what would have happened had we chosen differently). Yet, many times, our exposure to such feedback is systematically biased. For example, your friends are more likely to tell you about a party you missed if it was fun than if it was boring. Despite its prevalence, the effects of biased exposure to forgone outcomes on future choice have not been directly studied. In three studies (five experiments) using a simplified learning task, we study the basic influence of biased exposure to forgone outcomes in the extreme case in which decision makers can easily infer the missing information such that the biased exposure carries almost no informational value. The results in all studies suggest that nevertheless, the biased exposure to forgone outcomes affected choice. Exposure to forgone outcomes only when they were better than the obtained outcomes (Only-Better-Forgone) increased selections of the forgone option com-pared with exposure to forgone outcomes only when they were worse than the obtained outcome (Only-Worse-Forgone). Moreover, relative to an unbiased expo-sure to all forgone outcomes, the effect of exposure to Only-Worse-Forgone was larger than the effect of exposure to Only-Better-Forgone feedback. However, these effects were not universal: In environments that include rare negative events (“disasters”), biased exposure to forgone outcomes had very little effect. We raise potential explanations and further discuss implications for marketing and risk awareness..
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Plonsky, O., & Teodorescu K. (2020). Perceived patterns in decisions from experience and their influence on choice variability and policy diversification: A response to Ashby, Konstantinidis, & Yechiam, 2017. Acta Psychologica, 202. doi: 10.1016/j.actpsy.2019.102953
Click to expand abstract
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.
Download pdf. Direct link.
Cohen D., Plonsky, O., & Erev, I. (2020). On the Impact of Experience on Probability Weighting in Decisions Under Risk. Decision, 7(2), 153–162. doi: 10.1037/dec0000118
Click to expand abstract
Previous research demonstrates that feedback in decisions under risk leads people to behave as if they give less weight to rare events. We clarify the boundaries of this phenomenon and shed light on the underlying mechanisms. In a preregistered experiment, participants faced 60 different decisions-under-risk choice tasks. Each task was a choice between a safe prospect (e.g., “59 with certainty”) and a “rare disaster” gamble (“60 with p = .98; 10 otherwise”). Additionally, each option also incurred a small cost (a draw from the set {-8, -6, -4, -2, 0} was added to the payoff). After each choice, participants received full feedback concerning the (total) realized payoffs of each option. The experiment compared 2 conditions that differed in the dependency between the 2 added costs. The results reveal high sensitivity to this dependency. Underweight-ing of rare events (preference for the rare disaster gamble) emerged with experience only when this dependency implied that in most cases, the rare disaster alternative provides a higher outcome than the safe alternative. In contrast, when in most cases the final outcomes from the safer option were higher, feedback appeared to increase the weighting of rare events (i.e., increased preference for the safe option). Common decisions-under-risk models (e.g., prospect theory) that assume the value of each prospect is judged only as a function of its own payoff distribution cannot account for this difference. Yet, the results can be explained with the hypothesis that choice reflects reliance on small samples of past experiences with similar decision tasks.
Direct link.
Erev, I., Plonsky O., & Roth, Y. (2020). Complacency, panic, and the potential of gentle rule enforcement in addressing pandemics. Nature Human Behaviour, 4, 1095-1097. doi:10.1038/s41562-020-00939-z
Click to expand abstract
The impact of pandemics is magnified by the coexistence of two contradicting reactions to rare dire risks: panic and the ‘it won’t happen to me’ effect that hastens spread of the disease. We review research that clarifies the conditions that trigger the two biases, and we highlight the potential of gentle rule enforcement policies that can address these problematic conditions.
Direct link (full paper), Twitter thread
Roth, Y., Plonsky O., Shalev, E., & Erev, I. (2020). On the value of alert systems and gentle rule enforcement in addressing pandemics. Frontiers in Psychology, 11, 3252. doi: 10.3389/fpsyg.2020.577743
Click to expand abstract
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.
Direct link (full paper)
Plonsky O., Roth, Y., & Erev, I. (2021). Underweighting of rare events in social interactions and its implications to the design of voluntary health applications. Judgment and Decision Making, 16(2), 267-289.
Click to expand abstract
Research on small repeated decisions from experience suggests that people often behave as if they underweight rare events and choose the options that are frequently better. In a pandemic, this tendency implies complacency and reckless behavior. Furthermore, behavioral contagion exacerbates this problem. In two pre-registered experiments (Ntotal = 312), we validate these predictions and highlight a potential solution. Groups of participants played a repeated social game in one of two versions. In the basic version, people clearly preferred the dangerous reckless behavior that was better most of the time over the safer responsible behavior. In the augmented version, we gave participants an additional alternative abstracting the use of an application that frequently saves time but can sometimes have high costs. This alternative was stochastically dominated by the option abstracting the responsible choice and was thus normatively “irrelevant” to the decision participants made. Nevertheless, most participants chose the new (“irrelevant”) alternative, providing the first clear demonstration of underweighting of rare events in fully described social games. We discuss public policies that can make the responsible use of health applications better most of the time, thus helping them get traction despite being voluntary. In one field demonstration of this idea amid the COVID-19 pandemic, usage rates of a contact tracing application among nursing home employees more than tripled when using the app also started saving them little time on a daily basis, and the high usage rates sustained over at least four weeks.
Download pdf. 5 mins. presentation on this research (YouTube), Twitter thread
Plonsky, O., & Erev, I. (2021) To predict human choice, consider the context. Trends in Cognitive Sciences, 25(10), 819-820.
Click to expand abstract
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 con-text. This observation demonstrates how a focus on predictions can promote understanding of cognitive processes.
Direct link, Twitter thread
Teodorescu K., Plonsky, O., Ayal, S., & Barkan, R. (2021). Frequency of enforcement is more important than the severity of punishment in reducing violation behaviors. Proceedings of the National Academy of Sciences, 118(42), e2108507118. DOI: 10.1073/pnas.2108507118
Click to expand abstract
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 High probability of Inspection with Low Severity of fine (HILS) was more effective than an economically equivalent policy that combined Low probability of Inspection with High Severity of fine (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 non-criminal populations should consider increasing inspection rates rather than punishment severity.
Preprint, Direct link, Twitter thread, news coverage #1 (Hebrew), news coverage #2 (Hebrew)
Plonsky, O., Chen, D. L., Netzer L., Steiner, T., & Feldman, Y. (2022). Motivational drivers for serial position effects in high-stake legal decisions. Journal of Applied Psychology. DOI: 10.1037/apl0001064
Click to expand abstract
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.
Preprint, Direct link, Twitter thread.
Erev, I., Ert, E., Plonsky O., & Roth, Y. (2023). Contradictory deviations from maximization: Environment-specific biases or reflections of basic properties of human learning? Psychological Review, 130(3), 640-676. DOI: 10.1037/rev0000415
Click to expand abstract
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 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-specific and individual-specific decision biases.
Preprint. Direct link. 15 mins. presentation (Ido Erev, YouTube)
Selected working papers
Plonsky, O., Apel, R., Ert, E., Tennenholtz, M., Bourgin, D., Peterson, J. C., Reichman, D., Griffiths, T. L., Russell, S., Carter, E. C., Cavanagh, J. F., & Erev I. (working paper). Predicting Human Decisions with Behavioral Theories and Machine Learning. Preprint. Competition’s website. Competition’s data.
Plonsky, O. & Erev, I. (in press). Prediction oriented behavioral research and its relationship to classical decision research. preprint.
Plonsky, O., Ayoub, P., & Roth, Y. (working paper). Underweighting of rare events in strategic games. Preprint. 20 mins. conference presentation (YouTube)
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