Everyone Take Copies https://www.econlib.org/econlog/everyone-take-copies/
"… discs: Non-rivalrous goods are goods that can be used by multiple people without any loss to the other users. If participants exercise the ability to take a disc, then the original disc holder still has a disc and can still consume the full value of it.
… Participants discuss discs often enough to reveal how they conceptualize the resource. In many instances, they articulate the positive-sum logic of zero-marginal-cost copying. For example, … farmer Almond reasons, “ok so disks cant be stolen so everyone take copies,” explicitly rejecting the application of “stolen” to discs.
… Humans can state that digital piracy is illegal and take measures to prevent it. However, it will be difficult to cause an individual engaging in piracy to feel guilty as they do when they believe they are directly harming another human."
#economics #IntellectualProperty #ExperimentalEcon #ExperimentalLaw

Delegating in the Age of AI: Preferences for Decision Autonomy https://d.repec.org/n?u=RePEc:rco:dpaper:558&r=&r=eur
"… participants systematically underutilize both #AI and human agents, even when those agents outperform them. Despite a general hesitancy to delegate, we observe a clear preference for delegating to AI rather than human agents, a behavioral pattern that remains consistent across both decision domains and architectures
… suggesting that algorithm aversion stems primarily from a broader aversion to relinquishing control rather than from specific distrust towards AI
… If individuals are driven primarily by general reluctance to relinquish control rather than specific distrust in AI, then #transparency alone, focused narrowly on increasing #trust in AI, will likely fall short of overcoming this barrier."
#ExperimentalEcon #BoundedRationality

How do monetary incentives affect the measurement of social preferences? https://d.repec.org/n?u=RePEc:zur:econwp:482&r=&r=exp
"… the use of monetary #incentives, as well as the size of the stakes, have little impact on choices at the descriptive levels, as well as for the identification of qualitatively distinct preferences types. They appear to matter, however, for the quantitative identification of the strength and the precision of social preferences.
… the #socialPreferences of the general population are likely overestimated when elicited with hypothetical stakes. If one is solely interested in having a rough, descriptive measure of social preferences at the aggregate level, or if one wants to identify qualitatively distinct preferences types, then relying on hypothetical stakes might suffice.
… if one is interested in making a quantitative assessment of subjects’ other-regardingness, e.g., in order to make quantitative predictions, then our result suggest that using monetary incentives is advisable,
… no evidence that using a larger stake size improves the identification of social preferences"
#ExperimentalEcon #BehavioralEconomics

Against the Standard https://archiv.ub.uni-heidelberg.de/volltextserver/36879/8/Cubel_Against_dp764_2025.pdf
"… in the absence of feedback, women are less likely than men to benchmark their performance against a standard of excellence. This is inefficient because women who are likely to obtain increased rewards choose a low reward scheme instead.
… When feedback is provided and the standard is set by peers, this gender gap closes. However, the gap re-emerges, and even widens, when the standard of excellence is set by experts.
… If standards are set by experts and committees are perceived as male-dominated, a gender gap will exist in the award of promotions, grants or recognition. Understanding the differential impact of standards and feedback provision can help to design more inclusive competitive processes and bridge gender gaps in labour market outcomes."
#ExperimentalEcon #LaborEconomics #wages #gpg #LaborMarkets

Artificial intelligence, distributional #fairness, and pivotality https://d.repec.org/n?u=RePEc:hal:journl:hal-05165240&r=&r=ain
"#AI training introduces a significant shift – individual decisions no longer terminate with the present but… influence the future behavior of scalable algorithms. This amplifies the impact of individual actions, creating lasting #externalities. Yet, the aggregation of data from many individuals may lead to diffused #responsibility, weakening the sense of pivotality. … leading to less prosocial behavior compared to a situation with high perceived pivotality for algorithmic outcomes.
… removing pivotality led to increased #selfishness in how humans trained the algorithm. Importantly, this change in revealed #socialPreferences was driven by a shift in individual responsibility (the power over one’s own or others’ fate) rather than the incentive structures (the expected additional payoff of one’s current decisions through the AI’s training).
… findings reveal a positive correlation between participants’ beliefs about others’ revealed preferences in generating training data and their own AI training choices when they were pivotal for others’ payoffs. This pattern points to a potential #falseConsensus effect or belief distortion mechanism, where participants justify selfish behavior by assuming others are also selfish, rather than attempting to offset others’ selfishness through prosocial actions."
#ExperimentalEcon

Fairness Properties of Compensation Schemes https://d.repec.org/n?u=RePEc:ces:ceswps:_11943&r=&r=lma
"… the benefits of providing incentives need to be traded off against unintended side effects due to violation of employees’ #fairness norms
… pay inequality has a strong negative effect on perceived fairness. Controlling for pay #inequality, people consider piece rate schemes fairer than those with a discrete bonus and a tournament design
… if incentive contracts cannot be avoided, they should be designed carefully and motivated with reference to #proceduralFairness"
#LaborMarkets #wages
#ExperimentalEcon

Experimental Evidence on Attitudes Toward Inequality and Fairness https://www.annualreviews.org/content/journals/10.1146/annurev-economics-073124-083232
WP: https://www.danielweishaar.net/files/AHW2024_ExperimentalEvidence.pdf
"Although inequality aversion seems important in some contexts– particularly where resources are “manna from heaven”, many experiments also reveal #inequality acceptance, particularly in situations where available resources are linked to the discretionary production choices of individuals. Many people reward effort or productivity when making distributive choices, i.e., they are choice egalitarian or meritocratic. However, across the contexts that we have considered, there are also other prevalent #fairness positions, such as egalitarians, who object to inequalities regardless of how they come about, and libertarians, who accept market allocations and object to redistribution regardless of how inequality comes about."
#ExperimentalEcon #BoundedRationality

Noise and Bias: The Cognitive Roots of Economic Errors https://d.repec.org/n?u=RePEc:lan:wpaper:423483206&r=&r=exp
"… formalizes the idea that decision makers might follow a mixture of rules of behavior combining cognitively imprecise value maximization and computationally simpler shortcuts.
… findings suggest that neither cognitive imprecision nor multiplicity of behavioral rules suffice to explain received patterns in economic decision making.… jointly modeling (cognitive) noise in value maximization and #biases arising from simpler, cognitive shortcuts delivers a unified framework which can parsimoniously explain deviations from normative prescriptions across domains."
#BoundedRationality #heuristics #ExperimentalEcon

Who Gets the Callback? Generative AI and Gender Bias https://d.repec.org/n?u=RePEc:arx:papers:2504.21400&r=&r=lma
"… most #llm models reproduce stereotypical gender associations and systematically recommend equally qualified women for lower-wage roles, indicating occupational segregation.
… These biases stem from entrenched gender patterns in the training data as well as from an agreeableness bias induced during the reinforcement learning from human feedback stage
.…AI-driven hiring may perpetuate biases in the labor market and have implications for #fairness and diversity within firms"
#AI #jobtech #ExperimentalEcon #LaborEconomics #discrimination #bias