arXiv subjects

Matthew O. Jackson

Publications and source records attributed to Matthew O. Jackson.

At least 19 recordsLinked to original sources

arXiv2026

Social Network Structure, Wealth, and Wealth Inequality Across Cultures

Despite theory tying wealth inequality to social structure, empirical evidence has been limited to a few studies based on online social media data. This study uses a very different type of data, expands the global coverage to very different types of societies, and investigates new questions. In particular, we collect data from ~3500 sharing units (households) in 46 communities across the globe, representing considerable human social and cultural diversity. In each, we analyze the relationship between people's material wealth and the structure of social networks: borrowing money, sharing food, working together, socializing, etc. In almost all communities, a sharing unit's material wealth is positively associated with the number of other sharing units it both helps and is helped by. A sharing unit's wealth is also associated with the relative wealth of the sharing units to which it is linked---a form of economic homophily. Notably, communities with greater wealth inequality are also characterized by a network structure in which poorer sharing units are less well connected to wealthier ones. We augment our unique cross-cultural data with other community-level environmental, institutional, and economic attributes, opening new avenues for future research into the co-determination of wealth and social networks.

cs.SI↗

arXiv2026

Modeling the Structure of Human Behavior with AI Prompt Vectors

We introduce a general, easy-to-implement AI-based method for modeling and analyzing the structure and complexity of human behavior. We assign a large language model a "type vector" and then prompt it to choose actions across settings in which we observe human choices. For instance, the type vector (2, 4) becomes "You are a player characterized by the following profile: Altruism: 2 out of 5, Risk Aversion: 4 out of 5," after which it is prompted to make choices. We vary the dimensions (e.g., Altruism, Fairness, Trust,...) and values (e.g., 1-5) to minimize distance to human choices. Applying the method to 119,147 decisions made by 78,657 subjects from more than 35 countries across 10 classic economic game roles, we find that human behavior can be closely matched using three dimensions: Risk Aversion, Strategic Sophistication, and Trust. Moreover, the types needed to fit individuals across games cluster into fewer than a dozen groups, and can predict behavior in held-out games with different rules and available actions. The results suggest that behavior across diverse settings can be approximated by a low-dimensional, portable representation, supporting the possibility of general yet parsimonious theories across the behavioral sciences. More broadly, this new modeling method can provide insights into the structure of many human behaviors.

econ.TH↗

arXiv2026

The Fragility of Social Learning with Noisy Messages

We examine how agents learn when information from original sources only reaches them after noisy relay. A receiver learns if and only if they have access to sufficiently many chains of noisy relay and they perfectly understand the noise process. However, even slight uncertainty over message mutation rates makes learning from long chains impossible, no matter how many independent sources are accessed.

econ.TH↗

arXiv2026

The friendship paradox: Causal evidence of its behavioral consequences

We provide a first causal analysis of the behavioral consequences of the friendship paradox-the fact that people's friends in a network have more connections than average. We find that people's behavior is biased by their network position: they do not best respond to what they should infer the average behavior of the population to be, but instead simply to the average behavior of their friends. Moreover, we find that they fail to learn to overcome such a bias when relocated within the network, varying their observational environment. In these games of complements, the friendship paradox generates a systematic upward distortion in actions, increases behavioral dispersion, and persists despite learning opportunities.

econ.GN↗

arXiv2026

The Economic Benefits and Costs of AI and Policies to Mitigate AI's Impact on Inequality

We examine the economic impact of increasingly productive AI and policies that spread its benefits across the economy. Improvements in AI productivity trigger labor reallocation and changes in absolute and relative wages for different types of labor. Wages of labor that is essential for building AI increase faster than overall GDP. Wages of labor that is substituted for by AI decrease in both absolute and relative terms. Wages of labor that is used only in final goods production and is not displaced by AI increase in line with overall GDP. We contrast the impact of productivity gains depending on whether AI production is competitive or monopolistic. Monopoly production of AI restricts its deployment, slowing the transition and impact of AI. Optimal tax and regulatory policies that achieve Pareto-improvements differ depending on whether there is competition in AI production.

econ.GN↗

arXiv2026

BehaviorBench: Benchmarking Foundation Models for Behavioral Science Tasks

Foundation models have been increasingly applied to behavioral science domains such as psychology, sociology, and economics. While these models show promise in individual tasks such as survey response prediction and human-subject experiment simulation, there remains no systematic understanding of how well they perform across diverse behavioral science tasks, contexts, and populations. We introduce BehaviorBench, a comprehensive benchmark that evaluates foundation models along four core capabilities: (1) behavior prediction and simulation, (2) strategic decision-making, (3) subject-trait inference, and (4) behavioral knowledge application. Crucially, BehaviorBench evaluates model outputs at both the individual and distributional levels, capturing not only per-subject accuracy but also population-level alignment, an essential requirement for behavioral validity. Leveraging the tasks in BehaviorBench, we further develop Be.FM-1.5, extending the Be.FM family of behavioral foundation models fine-tuned on behavioral data. Our results reveal a considerable gap: proprietary general-purpose models excel at individual-level prediction and knowledge-intensive tasks, whereas behavioral foundation models, fine-tuned on behavioral data, achieve substantially stronger distributional alignment. Notably, Be.FM-1.5 leads on distributional metrics and remains competitive on individual-level metrics, suggesting that proper behavioral adaptation can close the gap. Our results highlight the importance of distributional evaluation, establish BehaviorBench as a foundation for developing and assessing behaviorally aligned AI systems, and demonstrate Be.FM-1.5's potential for a broad range of behavioral science studies. Our BehaviorBench and Be.FM-1.5 models can be accessed via https://umich-foreseer.github.io/behaviorbench/.

cs.CL↗

arXiv2026

Social Learning with Endogenous Information and the Countervailing Effects of Homophily

People learn about opportunities and actions by observing the experiences of their friends. We model how homophily -- the tendency to associate with similar others -- affects both the endogenous quality and diversity of the information accessible to decision makers. Homophily provides higher-quality information, since observing the payoffs of another person is more informative the more similar that person is to the decision maker. However, homophily can lead people to take actions that generate less information. We show how network connectivity influences the tradeoff between the endogenous quantity and quality of information. Although homophily hampers learning in sparse networks, it enhances learning in sufficiently dense networks.

econ.TH↗

arXiv2026

Network Beliefs and Behavior with Peer Effects

Individuals often act without knowing the full structure of the social network in which they are or will be embedded. We study how an individual's beliefs about their networks shapes their behavior when actions are peer interactive. Agents use what they know about the network to forecast their peers' actions. Those peers' actions depend on their beliefs, which then generate an iterative expression what we call "Iterative Belief Centrality." Agents' beliefs formed based on what they each see of the network are heterogeneous, depend on their network position, and can be correlated across connected agents. The resulting equilibrium behavior nests both complete-information and degree-based models as special cases, but more generally can differ systematically. If people's beliefs about the network satisfy a natural monotonicity condition in how connected they are, then belief iteration (fully rationally) amplifies behavioral differences across the network, increasing actions of more-connected and decreasing actions of less-connected agents relative to situations with homogeneous beliefs. We also show how positive correlation in people's positions in the network even further amplifies the variance of behavior. The framework provides a unified and tractable theory of network-based behavior with implications for many applications.

econ.TH↗

arXiv2026

AI Behavioral Science

We outline a foundation for a new field of ``AI Behavioral Science,'' covering three perspectives. First, as AI becomes ubiquitous and is increasingly proprietary and opaque, it becomes vital to develop techniques for assessing AI behavior. We outline how tools developed to assess people's behaviors by social scientists can be used to assess and infer AI's behaviors biases, tendencies, and heuristics. Second, we also discuss how AI can change the ways in which we learn about human behavior. Beyond its computational power, AI offers new techniques for simulating, inferring, and predicting human behaviors that we outline and discuss. Third, as humans and AI are interacting in increasingly complex and intertwined systems, we need to understand the implications for the resulting economic and political outcomes. We outline issues that are increasingly pressing concerning the future of human-AI interactions and potential changes and disruptions that can ensue.

cs.HC↗

arXiv2026

Peer Influence on West Point Cadets' Civil War Allegiances

Do social networks and peer influence shape major life decisions in highly polarized settings? We explore this question by examining how peers influenced the allegiances of West Point cadets during the American Civil War. Leveraging quasi-random variations in the proportion of cadets from Free States, we analyze how cadets' decisions about which army to join depended on the composition of their peers. We have three main findings. First, there was a strong and significant peer effect: a higher proportion of classmates from Free States significantly increased the likelihood that cadets from Slave States joined the Union Army. Second, the peer effect varies with geography, most notably with the slave population share in cadets' home states or counties, and with cadets' own slave ownership in 1860. Third, peer effects were amplified by shared experiences such as having served together in the Mexican-American War, continuous military service, and belonging to the same cohort, suggesting that sustained interaction is important.

econ.GN↗

arXiv2026

The Role of Referrals in Immobility, Inequality, and Inefficiency in Labor Markets

We study the consequences of job markets' heavy reliance on referrals. Referrals lead to more opportunities for workers to be hired, which lead to better matches and increased productivity, but also disadvantage job-seekers with few or no connections to employed workers, increasing inequality. Coupled with homophily, referrals also lead to immobility. We identify conditions under which distributing referrals more evenly reduces inequality and improves future productivity and mobility. We use the model to examine the short and long-run welfare impacts of policies such as affirmative action and algorithmic fairness.

econ.GN↗

arXiv2026

Interactions across multiple games: cooperation, corruption, and organizational design

Teamwork is vital in many settings, and it is socially beneficial for teams to cooperate in some situations (``good games'') and not in others (``bad games;'' e.g., those that allow for corruption). A team's cooperation in any given game depends on expectations of cooperation in future iterations of both good and bad games. We identify when sustaining cooperation on good games necessitates cooperation on bad games. We then characterize how a designer should optimally assign workers to teams and teams to tasks that involve varying arrival rates of good and bad games. Our results show how organizational design can be used to promote cooperation while minimizing corruption.

econ.TH↗

arXiv2026

Supply Chain Disruptions, the Structure of Production Networks, and the Impact of Globalization

We introduce a parsimonious multi-sector model of international production and use it to study the impact of a disruption in the production of some goods propagates to other goods and consumers, and how that impact depends on the goods' positions in, and overall structure of, the production network. We show that the short-run impact of a disruption can be dramatically larger than the long-run impact. The short-run disruption depends on the value of all of the final goods whose supply chains involve a disrupted good, while by contrast the long-run disruption depends only on the cost of the disrupted goods. We use the model to show how increased complexity of supply chains leads to increased fragility in terms of the probability and expected short-run size of a disruption. We also show how decreased transportation costs can lead to increased specialization in production, lowering the chances for disruption but increasing the impact conditional upon disruption. We use the model to characterize the power that a country has over others via diversions of its production as well as quotas on imports and exports.

econ.GN↗

arXiv2025

Multiplexing in Networks and Diffusion

Social and economic networks are often multiplexed, meaning that people are connected by different types of relationships -- such as borrowing goods and giving advice. We make two contributions to the study of multiplexing and the understanding of simple versus complex contagion. On the theoretical side, we introduce a model and theoretical results about diffusion in multiplex networks. We show that multiplexing impedes the spread of simple contagions, such as diseases or basic information that only require one interaction to transmit an infection. We show, however that multiplexing enhances the spread of a complex contagion when infection rates are low, but then impedes complex contagion if infection rates become high. On the empirical side, we document empirical multiplexing patterns in Indian village data. We show that relationships such as socializing, advising, helping, and lending are correlated but distinct, while commonly used proxies for networks based on ethnicity and geography are nearly uncorrelated with actual relationships. We also show that these layers and their overlap affect information diffusion in a field experiment. The advice network is the best predictor of diffusion, but combining layers improves predictions further. Villages with greater overlap between layers -- more multiplexing -- experience less overall diffusion. Finally, we identify differences in multiplexing by gender and connectedness. These have implications for inequality in diffusion-mediated outcomes such as access to information and adherence to norms.

econ.GN↗

arXiv2025

Optimal Regulation and Investment Incentives in Financial Networks

We examine optimal regulation of financial networks with debt interdependencies between financial firms. We first show that firms often have an incentive to choose excessively risky portfolios and overly correlate their portfolios with those of their counterparties. We then characterize how optimal regulation depends on a firm's financial centrality and its available investment opportunities. In standard core-periphery networks, optimal regulation depends non-monotonically on the correlation of banks' investments, with maximal restrictions for intermediate levels of correlation. Moreover, it can be uniquely optimal to treat banks asymmetrically: restricting the investments of one core bank while allowing an otherwise identical core bank (in all aspects, including network centrality) to invest freely.

econ.GN↗

arXiv2025

Using Large Language Models to Categorize Strategic Situations and Decipher Motivations Behind Human Behaviors

By varying prompts to a large language model, we can elicit the full range of human behaviors in a variety of different scenarios in classic economic games. By analyzing which prompts elicit which behaviors, we can categorize and compare different strategic situations, which can also help provide insight into what different economic scenarios induce people to think about. We discuss how this provides a first step towards a non-standard method of inferring (deciphering) the motivations behind the human behaviors. We also show how this deciphering process can be used to categorize differences in the behavioral tendencies of different populations.

cs.AI↗

arXiv2025

Inequality's Economic and Social Roots: the Role of Social Networks and Homophily

I discuss economic and social sources of inequality and elaborate on the role of social networks in inequality, economic immobility, and economic inefficiencies. The lens of social networks clarifies how the entanglement of people's information, opportunities, and behaviors with those of their friends and family leads to persistent differences across communities, resulting in inequality in education, employment, income, health, and wealth. The key role of homophily in separating groups within the network is highlighted. A network perspective's policy implications differ substantially from a narrower economic perspective that ignores social structure. I discuss the importance of ``policy cocktails'' that include aspects that are aimed at both the economic and social forces driving inequality.

econ.GN↗

arXiv2025

Experimenting with Networks

We provide an overview of methods for designing and implementing experiments (field, lab, hybrid, and natural) when there are networks of interactions between subjects.

econ.GN↗