We conduct an incentivized laboratory experiment to study people's perception of generative artificial intelligence (GenAI) alignment in the context of economic decision-making. Using a panel of economic problems spanning the domains of risk, time preference, social preference, and strategic interactions, we ask human subjects to make choices for themselves and to predict the choices made by GenAI on behalf of a human user. These problems confront agents with trade-offs (e.g., higher payoff vs. earlier payoff, efficiency vs. equity, riskier but potentially higher rewards vs. safer but lower rewards) and the optimal choices depend on the agent's preferences. We find that people overestimate the degree to which GenAI choices are aligned with human preferences in general (anthropomorphic projection), and with their personal references in particular (self projection). On average, human subjects' predictions about GenAI's choices in every decision environment are much closer to the average human-subject choice than to the average GenAI choice. At the individual level, human subjects' predictions about GenAI's choices in a given environment are highly correlated with their own choices in the same environment.

In recent years, a growing number of students are being assigned to schools through centralized clearinghouses. The success of such clearinghouses crucially relies on the use of a stable matching mechanism [10,12]. The matching market design literature finds that a designer who wishes to implement a stable allocation has limited scope for further design. First, the rural hospital theorem determines that the same positions are filled in all stable allocations [7,9]. Second, the set of stable allocations has the consensus property: all students prefer the outcome of the student-proposing deferred acceptance mechanism (henceforth) to any other stable allocation [4,8]. Third, empirical and theoretical studies suggest that all students, save for a handful, receive the same assignment in all stable allocations [e.g., 1, 2, 5 , 6, 11]. This last finding implies that schools have limited incentive to collect information and to misreport their preferences [3].

Many economic-theoretic models incorporate finiteness assumptions that, while introduced for simplicity, play a real role in the analysis. Such assumptions introduce a conceptual problem, as results that rely on finiteness are often implicitly nonrobust; for example, they may depend upon edge effects or artificial boundary conditions. Here, we present a unified method that enables us to remove finiteness assumptions, such as those on datasets, market sizes, and time horizons. We then apply our approach to a variety of revealed preference, matching, and exchange economy settings.

A growing body of evidence suggests that many people struggle with decision-making in the presence of correlation. In typical examples of this problem, decision-makers are presented with multiple signals that are each influenced both by independent components and information from a common source. The process by which signals are generated induces correlation, and optimal decision-making requires taking it into account. In practice, however, experiments like those of Enke and Zimmermann [1] demonstrate that many decision-makers neglect to do so, effectively acting as if these correlated signals are independent.

Prior to 2014, the admission to Master's and PhD programs in psychology in Israel was a mostly decentralized process. In 2013, in response to concerns about the existing procedure, we proposed to use a mechanism that is both stable and strategy-proof for applicants. The first part of this paper describes how we successfully centralized this market, and the critical role of recent advances in the theory of matching with contracts. In the second part of the paper we show empirically (using clearinghouse data) and theoretically that the regularity in preferences with respect to contractual terms leads to a large core. Our results stand in sharp contrast to findings of previous studies on two-sided matching markets without contracts [2, 10, 11, 13]. During the design of the Israeli Psychology Master's Match (IPMM), we met with the faculty of each of the participating programs and asked about the way they choose between applicants. We discovered that departments' choice functions cannot be summarized by a quota and a rank-ordered list (ROL) for each program. Some departments employ affirmative action through minority quotas. Others aim to equalize the number of advisees each faculty member receives. And finally, some departments are willing to admit a limited number of applicants with different contractual terms (e.g., funding). Since terms can alter preferences between programs, this last feature implies that in order to satisfy the aforementioned desiderata, the applicants' message space must be expressive enough to convey their preferences over program-terms pairs. This market is therefore a special case of the matching-with-contracts model [9].

A critical, yet underappreciated feature of market design is that centralized markets operate within a broader economic context; often market designers cannot force participants to join a centralized market. As such, well-designed centralized markets must induce participants to join voluntarily, in spite of pre-existing decentralized institutions they may already be using. Utilizing the general framework of Monderer and Tennenholtz (2006), we take the view that centralizing a market is akin to designing a mediator to which people may sign away their decision rights. The mediator is voluntary in the sense that it cannot condition the actions of those who participate on the actions of those who do not.

A mechanism is said to be strategy-proof if no agent has an incentive to misrepresent her true preferences. This property is considered highly desirable for mechanisms that are used in real-life markets. And indeed, many of the great success stories of market design employ strategy-proof mechanisms, such as the second-price sealed-bid auction (Vickrey 1961), or Deferred Acceptance (DA, Gale and Shapley 1962; Dubins and Freedman 1981; Roth 1982). Specifically, in school-choice settings, the appeal of strategy-proof mechanisms is one of the main reasons many school districts choose the applicant-proposing version of DA over pre-existing mechanisms. At the core of the attractiveness of these mechanisms is the assumption that agents report their preferences truthfully in strategy-proof environments.