Christoph Huber et al. (2023), Competition and moral behavior: A meta-analysis of 45 crowd-sourced experimental designs, forthcoming in PNAS


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.


Bonder, T, Erev I, Ludvig A.E, Roth Y. (2023),  The common origin of both oversimplified and overly complex decision rules, Journal of Behavioral Decision Making, e2321.


Doron S, Lahav Y, Roth, Y. (2022), Reaching for Returns in Retail Structured Investment, Management Science, 68(1).


Erev I, Hiller M, Klobner S, Lifshitz G, Martines V, Roth Y. (2022),

Promoting Healthy Behavior through Repeated Deposit Contracts: An Intervention Study, Journal of Experimental Psychology, 92. 


Grossman, et al., (2022), Insights into accuracy of social scientists' forecasts of societal change. Nature Human Behaviour, 7(4), 484-501.


Haruvy, E., & Roth, Y. (2022), On the Impact of an Intermediary Agent in the Ultimatum Game. Games, 13(3), 43.


Erev, I., Roth, Y., & Sonsino, D. (2022), Decisions from valuations of unknown payoff distributions. Decision, 9(2), 172–193.


Shavit, Y., Roth, Y., Busemeyer, J., & Teodorescu, K. (2022), Intertemporal decisions from experience versus description: Similarities and differences. Decision, 9(2), 131–152.

Shavit, Y., Roth, Y., & Teodorescu, K. (2021), Promoting Healthy Eating Behaviors by Incentivizing Exploration of Healthy Alternatives. Frontiers in Nutrition, 8, 277. 

Sonsino, D., Lahav, Y., & Roth, Y. (2020), Reaching for Returns in Retail Structured Investment. Management Science, articles in advance. 

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. 

Roth Y. (2020), The decision to check in multialternative choices and limited sensitivity to default, Journal of Behavioral Decision Making, 33(5), 643-656.  

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.

Erev, I., Plonsky, O., & Roth, Y. (2020), Complacency, panic, and the value of gentle rule enforcement in addressing pandemics, Nature Human Behaviour, 4(11), 1095-1097.

Chebat E, Y Roth, JC Chebat (2020), How Culture Moderates the Effects of Justice in Service Recovery, Review of Marketing Science

Erev I, G Gilboa Freedman, & Roth Y. (2019), The impact of rewarding medium effort and the role of sample size, Journal of Behavioral Decision Making, 32(5), 507-520.

Roth Y. (2016) Do Brands Serve as Reliable Signals of Nutritional Quality? The Case of Breakfast Cereals, Journal of Food Products Marketing, 23(1), 1-23. 

Roth Y, M Wänke, Erev I. (2016) Click or Skip: The Role of Experience in Easy-Click Checking Decisions,  Journal of Consumer Research, 43(4), 583–597.

Chebat JC, O Erradey, C Gélinas-Chebat, Y Roth. (2015) A sensory approach to brand confusion,  Journal of Brand Strategy, 5(1), 101-115.

Recent Publication

Contradictory Deviations from Maximization: Environment-Specific Biases, or Reflections of Basic Properties of Human Learning? 

Ido Erev, Eyal Ert, Ori Plonsky & Yefim Roth

Psychological Review 2023


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. The 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 often 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 does not change. Moreover, our analysis shows that the direction of these deviations can be captured with simple “partially attentive sampler” models. These models differ from the popular reinforcement learning models in two ways: They assume that choice propensities reflect reliance on small samples (rather than temporal difference learning), and a partially attentive choice rule (rather than softmax or epsilon-greedy rules). A three-parameter abstraction of these assumptions, estimated to fit the contradictory deviations we studied, provides useful prediction of choice behavior in a preregistered study with 40 new randomly selected choice tasks. These results suggest that ignoring the predictable impact of the incentive structure can lead to exaggeration of the importance of environment-specific decision biases. We propose that the descriptive value of the reliance on small samples assumption reflects the fact that, in static settings, this assumption approximates the output of complex cognitive processes that resemble machine learning classification algorithms, and that features of the choice environment can impact the likely classification rules.