Cheating in the Lab Predicts Fraud in the Field: An Experiment in Public Transportation

You’ve Got Mail: A Randomized Field Experiment on Tax Evasion

We report from a large-scale randomized field experiment conducted on a unique sample of more than 15,000 taxpayers in Norway who were likely to have misreported their foreign income. By randomly manipulating a letter from the tax authorities, we cleanly identify that moral suasion and the perceived detection probability play a crucial role in shaping taxpayer behavior. The moral letter mainly works on the intensive margin, while the detection letter has a strong effect on the extensive margin. We further show that only the detection letter has long-term effects on tax compliance. This paper was accepted by Yan Chen, behavioral economics.

How Field Experiments in Economics Can Complement Psychological Research on Judgment Biases

This review summarizes results of field experiments examining individual behaviors across several market settings—from open-air markets to rideshare markets to tax-compliance markets—where people sort themselves into market roles wherein they make consequential decisions. Using three distinct examples from my own research on the endowment effect, left-digit bias, and omission bias, I showcase how field experiments can help researchers understand mediators, heterogeneity, and causal moderation involved in judgment biases in the field. In this manner, the review highlights that economic field experiments can serve an invaluable intellectual role alongside traditional laboratory research.

Integrative experiments identify how punishment affects welfare in public goods games

Despite decades of research, the conditions under which punishment promotes cooperation remain unclear. Through an integrative experiment varying 14 design parameters of public goods games across 360 experimental conditions (147,618 decisions from 7100 participants), we reveal substantial heterogeneity in punishment effectiveness: Its impact on welfare ranges from 43% improvement to 44% reduction depending on the game parameters. To characterize these patterns, we developed models that outperformed human forecasters in predicting punishment effectiveness in new experiments. Communication emerges as the most important factor, followed by contribution framing (opt out versus opt in), contribution type (variable versus all-or-nothing), game length, and outcome visibility, though these factors often interact. The results reframe the debate from whether punishment works to when it does, demonstrating how integrative experiments enable discovery of generalizable patterns in social phenomena. , Editor’s summary People face conflicts between maximizing personal gain versus supporting collective interests. If we cooperatively recycle or donate to charities, it benefits society, but it also costs us time and resources that could be selfishly preserved for ourselves. We impose penalties to deter those undesirable or selfish behaviors, but under what conditions do punishments or penalties effectively modify behavior to benefit group welfare? Alsobay et al . systematically and simultaneously varied 14 factors together instead of in isolation. Punishment was unequivocally most effective when paired with consistent communication, particularly over time. Another effective factor was “opting out” or withdrawing some, but not all, endowments already in the public fund. These methodological advances revealed when, rather than whether, punishment works. —Ekeoma Uzogara , INTRODUCTION Human societies face many situations where individual and collective interests conflict, often referred to as social dilemmas. Costly peer punishment has been studied for more than 25 years in public goods games (stylized behavioral experiments in which individuals decide how much to contribute to a shared pool that benefits everyone) as a mechanism to promote cooperation. Prior research has identified many contextual factors that moderate punishment’s effectiveness, including game length, communication, group size, punishment cost, and so on. However, the specific conditions under which punishment improves group welfare remain unclear. RATIONALE We argue that this lack of clarity derives from the dominant experimental paradigm, in which any given study manipulates only one or a few theoretically informed factors. Because such studies differ in many ways (different experimental procedures, populations), their results are often difficult to compare or integrate. Consequently, one can list many factors that have some effect, but cannot say how much each matters relative to the others, or how they work together, and as a result, cannot predict when punishment will help or harm welfare in new settings. To address this fundamental knowledge gap, we use an integrative experimental design and systematically vary 14 parameters across 360 conditions (147,618 decisions from 7100 participants) to elucidate when punishment improves versus undermines welfare in public goods games, which factors matter most, and how they interact. RESULTS The effect of punishment on welfare ranged from 43% improvement to 44% reduction depending on the specific combination of game parameters. To characterize this heterogeneity, we trained a model that outperformed all 553 human forecasters (laypeople and experts) in predicting whether punishment would help or harm welfare in new experiments. Communication emerged as roughly three times more important than any other factor, followed by contribution framing (opt in versus opt out), contribution type (variable versus all-or-nothing), game length, and peer outcome visibility (whether participants can see others’ earnings). These factors often interact. For example, longer games enhance punishment’s effectiveness only when communication is available, and contribution framing effects depend on both contribution type and outcome visibility. CONCLUSION Many phenomena in social science are shaped by many factors whose interactions are consequential, yet the dominant experimental paradigm often limits its inquiry to “does a given effect exist?” and examines hypothesized factors in isolation. As a result, research programs can accumulate many partial explanations without a clear picture of how they combine to determine outcomes across settings. Knowing that factors matter individually is fundamentally different from knowing how much each matters and how they interact. The integrative approach implemented here offers one way forward. It varies many factors simultaneously within a shared design space, evaluates models by their predictive accuracy on new experiments, and probes those models to constrain and develop theory. Our hope is that integrative experiment designs, combined with models that integrate prediction and explanation, represent a path toward more cumulative social science. Integrative experiment reveals when punishment helps versus harms. We systematically varied 14 design parameters across 360 experimental conditions. The effect of punishment on cooperation efficiency ranged from −44% to +43% depending on the specific game parameters. Communication emerged as three times more important than any other factor, followed by contribution framing, contribution type, and game length.

Mis-Nudging Morality

Morals constrain self-serving behavior. Yet, self-regulation failures in the face of monetary temptation are common at the workplace. To limit such failures, organizations can design environments that limit the temptation to behave self-servingly, nudging workers to uphold their morals. In a series of experiments where participants may be tempted to take excessive pay after exerting effort, we study whether a simple intervention—asking individuals to state the wage they believe should be paid ex ante, before facing the temptation to take excessive compensation—prevents self-serving behavior. In contrast to lay beliefs and the predictions from prior work, we find that such an intervention is not effective, leading to self-serving behavior. However, a more realistic elicitation procedure of the appropriate wage mitigates this effect. These findings contribute to work on the malleability of moral behavior showing that simple interventions thought to effectively mitigate self-serving behavior can prompt individuals to stretch their moral boundaries. They also stress the importance of properly testing interventions that might seem intuitive. This paper was accepted by Yan Chen, behavioral economics and decision analysis. Funding: Financial support from the Israel Science Foundation [Grant 766/19] is gratefully acknowledged. Supplemental Material: The online appendix and data are available at https://doi.org/10.1287/mnsc.2022.4344 .

We don’t have a hundred biases, we have the wrong model

Behavioral economics has identified dozens of cognitive biases that stop us from acting 'rationally'. But instead of building up a messier and messier picture of human behavior, we need a new model.

The Effects of Financial Incentives in Experiments: A Review and Capital-Labor-Production Framework

We review 74 experiments with no, low, or high performance-based financial incentives. The modal result has no effect on mean performance (though variance is usually reduced by higher payment). Higher incentive does improve performance often, typically judgment tasks that are responsive to better effort. Incentives also reduce “presentation” effects (e.g., generosity and risk-seeking). Incentive effects are comparable to effects of other variables, particularly “cognitive capital” and task “production” demands, and interact with those variables, so a narrow-minded focus on incentives alone is misguided. We also note that no replicated study has made rationality violations disappear purely by raising incentives.

Reason-Mediated Behavioral Models for Auditing LLM Social Simulators

Large language models are increasingly used as social simulators, including as synthetic survey respondents. Most evaluations ask whether simulated outcomes resemble human outcomes. We argue that this is necessary but too weak: a simulator can match the final answer while using the wrong behavioral mechanism. We study this problem through a 94-person sunscreen concept test in which each respondent evaluated three product concepts and wrote open-ended rationales. We map those rationales into signed reason states $Z$, where positive signs support adoption and negative signs block it. This gives a practical audit: holding respondent descriptors $D$, category context $K$, and concept treatment $X$ fixed, do human-stated reasons help predict behavior $Y$, and can an LLM simulate the same reason state without seeing the human rationale or outcome? Human-stated reasons substantially improve held-out prediction of purchase intent. LLM-simulated reasons are more brittle: they often sound plausible, but frequently echo the concept board rather than recover the respondent's acceptance or rejection path. The paper contributes an evaluation framework for social simulators. Reason states do not identify natural causal effects by themselves, but they provide an interpretable test of whether a simulator's stated reasons align with human evidence.

Unwillingness to pay for privacy: A field experiment

We measure willingness to pay for privacy in a field experiment. Participants bought at most one DVD from one of two competing online stores. One store consistently required more sensitive personal data than the other, but otherwise the stores were identical. In one treatment, DVDs were one Euro cheaper at the store requesting more personal information, and almost all buyers chose the cheaper store. Surprisingly, in the second treatment when prices were identical, participants bought from both shops equally often.

Moral Incentives in Credit Card Debt Repayment: Evidence from a Field Experiment

We study the role of morality in debt repayment, using an experiment with the credit card customers of a large Islamic bank in Indonesia. In our main treatment, clients receive a text message stating that “non-repayment of debts by someone who is able to repay is an injustice.” This moral appeal decreases delinquency by 4.4 percentage points from a baseline of 66 percent and reduces default among customers with the highest ex ante credit risk. Additional treatments help benchmark the effects against direct financial incentives and rule out competing explanations, such as reminder effects, priming religion, and provision of new information.

Generative Ethics: How AI Model Choice Shapes Social and Financial Performance

This paper estimates potential effects of AI adoption on firms' ethical decision making. Here I focus on variation in models' partiality, defined as the weight given to the party it serves relative to a third party. Study 1 applies an established instrument from computer science, administering 5,000 autonomous-vehicle dilemmas to each of fifteen frontier models and recovering a measure of moral partiality. Models differ sharply. All favor third parties over their principal, but by very different amounts. Some are nearly indifferent, while others will let up to five passengers die rather than kill a single pedestrian. Study 2 casts the same models as business advisors choosing, across 260 pairs, between projected firm revenue and jobs created outside the firm. From each model's choices, I estimate how much revenue it will give up per job. Twelve models pay for community employment, at rates from $923 to $7,066 per job; two place no measurable weight on it; and one, xAI's Grok, requires additional revenue before it will accept community jobs at all. Partiality in the vehicle dilemmas predicts how much revenue models give up for community jobs (ρ = .725, p = .002). AI thus introduces a new source of variation in how firms balance their objectives, and which model a firm adopts is a decision about whose interests the organization will prioritize.

When Convenient Illusions Hold Precedence...

Language, Substance, and Money are intermediaries which crumble under weight of inspection

Rethinking Trust

Reprint: R0906H Will we ever learn? We’d barely recovered from Enron and WorldCom before we faced the subprime mortgage meltdown and more scandals that shook our trust in businesspeople. Which raises the question: Do we trust too much? In this article, Stanford professor and social psychologist Kramer explores the reasons we trust so easily—and, often, so unwisely. He explains that genetics and childhood learning make us predisposed to trust and that it’s been a good survival mechanism. That said, our willingness to trust makes us vulnerable. Our sense of trust kicks in on remarkably simple cues, such as when people look like us or are part of our social group. We also rely on third parties to verify the character of others, sometimes to our detriment (as the victims of Bernard Madoff learned). Add in our illusions of invulnerability and our tendencies to see what we want to see and to overestimate our own judgment, and the bottom line is that we’re often easily fooled. We need to develop tempered trust. For those who trust too much, that means reading cues better; for the distrustful, it means developing more receptive behaviors. Everyone should start with small acts of trust that encourage reciprocity and build up. Having a hedge against potential abuses also helps. Hollywood scriptwriters, for instance, register their treatments with the Writers Guild of America to prevent their ideas from being stolen by the executives they pitch. To attract the right relationships, people must strongly signal their own honesty, proactively allay concerns, and, if their trust is abused, retaliate. Trusting individuals in certain roles, which essentially means trusting the system that selects and trains them, also works but isn’t foolproof. And don’t count on due diligence alone for protection; constant vigilance is needed to make sure the landscape hasn’t changed.

Behavioral Economics: Past, Present, and Future

(July 2016)

Payment Rewards and Credit Card Debt: Experimental Evidence

We report on a controlled laboratory experiment in which participants make consumption, saving, and credit card repayment decisions when credit card purchases e

The Liar’s Dividend: Can Politicians Claim Misinformation to Evade Accountability?

This study addresses the phenomenon of misinformation about misinformation, or politicians "crying wolf"' over fake news. Strategic and false claims that stories are fake news or deepfakes may benefit politicians by helping them maintain support after a scandal. We posit that this benefit, known as the "liar's dividend," may be achieved through two politician strategies: by invoking informational uncertainty or by encouraging oppositional rallying of core supporters. We administer five survey experiments to over 15,000 American adults detailing hypothetical politician responses to stories describing real politician scandals. We find that claims of misinformation representing both strategies raise politician support across partisan subgroups. These strategies are effective against text-based reports of scandals, but are largely ineffective against video evidence and do not reduce general trust in media. Finally, these false claims produce greater dividends for politicians than alternative responses to scandal, such as remaining silent or apologizing.

The Reversal Test: Eliminating Status Quo Bias in Applied Ethics

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