Abstract:  In economic settings such as learning, social behavior, and financial contagion, agents interact through interdependent networks. This paper examines how a decision maker (DM) can design an optimal intervention strategy under network uncertainty, modeled as a zero-sum game against an adversarial "Nature" that reconfigures the network within an uncertainty set. Using duality, we characterize the DM's unique robust intervention and identify the worst-case network structure, which exhibits a rank-1 property, concentrating risk along the intervention strategy. We analyze the costs of robustness, distinguishing between global and local uncertainty, and examine the role of higher-order uncertainties in shaping intervention outcomes. Our findings highlight key trade-offs between maximizing influence and mitigating uncertainty, offering insights into robust decision-making. This framework has applications in policy design, economic regulation, and strategic interventions in dynamic networks, ensuring their resilience against uncertainty in network structures.

Abstract:  This paper explores the use of Large Language Models (LLMs) as decision aids, focusing on models' ability to learn preferences and provide personalized recommendations. To establish a baseline, we replicate standard economic experiments on choice under risk (Choi et al., 2007) with GPT, one of the most prominent LLMs, prompted to respond as (i) a human decision maker or (ii) a recommendation system for customers. With these baselines established, GPT is provided with a sample set of choices and prompted to make recommendations based on the provided data. From the data generated by GPT, we identify its (revealed) preferences and explore its ability to learn from data. Our analysis yields three results. First, GPT's choices are consistent with (expected) utility maximization theory. Second, GPT can align its recommendations with people's risk aversion, by recommending less risky portfolios to more risk-averse decision makers, highlighting GPT's potential as a personalized decision aid. Third, however, GPT demonstrates limited alignment when it comes to disappointment aversion.

Abstract:  Recent literature highlights that social choice in laboratory games can vary substantially across societies. Researchers have speculated, without direct evidence, that this may be partly due to cultural variation in focal points for decision-making. Here, we provide the first direct evidence of a focal point that influences social choice in one culture but not another. In Confucian-influenced cultures, and especially in South Korea, social structure is influenced by age hierarchies. In fact, age hierarchies are integrated directly into Korean linguistics, with age differences of even just one year dictating the respect levels, titles, formalities, and grammar that must be used between two individuals. Therefore, even small age differences are salient and relevant in virtually all Korean social contexts. This paper examines the role of age information on social choice in Korea and the US, using a coordination game, the Stag Hunt (SH), and a cooperation game, the Prisoner’s Dilemma (PD). We find that age information increased coordination in SH and cooperation in PD among Korean participants, primarily by increasing reciprocity to expected partner choices. Conversely, age information had 0% effects in American participants, thus providing the literature’s first direct evidence of a focal point for social decision-making that matters in one culture but not another. Moreover, baseline cooperation rates in PD were higher in the US (53%) than in Korea (46%), but these differences disappeared once age was provided (53% in both countries). This is because without age information, Korean participants were deprived of information that typically guides their decision-making. This cautions that cross-cultural researchers must account for culture-specific focal points in order to avoid asymmetry in external validity when comparing across societies.

Abstract:  We investigate the effects of individuals' strategic sophistication measured by level-k type on collective action in the context of social distancing during the early-stage of the COVID-19 pandemic. We build a weakest-link public goods game with a private cost of social distancing, in which agents are heterogeneous in level-k types. We find that players with higher level-k types are more likely to engage in social-distancing behaviors. We test this hypothesis with large-scale nationally representative survey data that measured level-k types through incentivized experiments. Our empirical findings provide consistent evidence with our theoretical prediction. This study sheds new light on the role of the level-k theory in understanding real-world collective action problems.

Abstract:  We investigate the effects of a semester-long student-centered teaching program on prosocial giving and network homophily by conducting pre- and post-intervention friendship surveys and dictator game experiments. Using a difference-in-differences method, we show that the intervention promotes prosocial giving to indirectly connected peer students, strengthens class and gender homophily in friendship, and reduces average distance among indirectly connected students. By structurally estimating a network formation model, we infer that the program also reduces costs of making friends of both genders in the same class. Our findings offer novel micro-level insights on the connection between teaching practices and social capital and networks.

Abstract:  We evaluate a program wherein an adaptive intelligent tutoring system, coupled with increased teacher engagement was applied to students’ interactive learning processes and mathematics achievement outcomes. A quasi-experimental design was utilized to analyze the effectiveness of a one-semester program implemented during regular mathematics classes for Grade 7 students in Vietnam. The intervention resulted in overall positive and significant learning gains, including evidence of direct teacher impacts on mathematics achievement, along with moderate improvement in the students’ perception of teacher efficacy and classroom interaction. Learning gains were greater for low-performing students at the baseline math performance. These results point to an effective solution for achieving quality education to improve learning outcomes.

Abstract:  We study a pandemic disease control problem in which an infectious disease like COVID-19 spreads over multiple communities and non-pharmaceutical interventions are employed to control the spread of the disease. Drawing upon a canonical susceptible-infected-recovered model, we formulate the problem as a discrete time, finite horizon, dynamic program. As a base model, we first develop an epidemic model in which one benevolent social planner uses non-pharmaceutical interventions to control the spread of disease within one community. We extend this base epidemic model to a more complex pandemic model that additionally considers the interactions between multiple communities. We derive the optimal policies for both epidemic and pandemic models and characterize their structural properties using analytical and computational approaches. For the pandemic model, we propose three different principles under which a social planner enforces intervention orders. We compare the optimal policies for the three different principles and discuss related managerial implications.