TL;DR
This work develops a multiagent simulation of a popular social network, Reddit, and uses millions of posts from users on the platform to model content-sharing on the platform.
Social media recommendation systems create conflict: content producers, content consumers, platform operators, and social pressures struggle to direct the allocation of attention in their favor while also facing competing societal pressures. Producers may feel pressure to optimize for recommendation algorithms and consumers may be exposed to content with negative externalities such as polarization and misinformation. Platforms aim to maximize user engagement, oftentimes resulting in over consumption from consumers. There is mounting pressure from policymakers and broader society to address these issues. Prior methods for constructing social media feeds have focused on recommendation systems. Instead, we propose a, to our knowledge, novel auction based method for feed construction where users bid for the attention of other users. Our mechanism systematically considers producers, consumers, platform operators, and social welfare. We show that our auction is weakly incentive compatible under budget constraints. To balance between producer and consumer welfare, we introduce a tax policy to the auction to increase the cost of content with negative externalities. Simulations over common social network topologies and an empirically observed network show how different feed algorithms prioritize different stakeholders. Our proposed auction based mechanisms produce on average 36.3% higher producer welfare than comparison algorithms on the empirically observed network and 31.4% higher producer welfare than comparison algorithms on the synthetic networks. Our methods also produce more equitable distributions of attention than baseline methods across all evaluated network types. Our proposed mechanism addresses attention allocation at a systematic level, balancing between the needs of different stakeholders.
This paper studies how network structures affect the efficiency of information aggregation in social learning environments. We consider a model in which rational agents sequentially choose actions based on private signals and observations of their neighbors'actions in a network. Focusing on comparisons of expected payoffs at a given finite period, we show that there exists an information structure under which the star network achieves a strictly higher expected payoff than any other network, and another information structure under which the complete network achieves a strictly higher expected payoff than any other network. Taken together, these results imply that no network is uniformly optimal across all information structures. Our analysis highlights a trade-off between the responsiveness effect and the overturning effect: disconnected networks preserve responsiveness of actions to private signals, whereas highly connected networks facilitate the aggregation of extreme information that overturns public beliefs.
Recommender systems shape which content reaches users, making it important to measure whether coordinated activity gains visibility beyond the accounts that initiate it. Existing robustness evaluations largely focus on static target-rank changes and do not capture how coordinated interactions, recommendation, and user response evolve within a feedback loop. We propose CoSimRec, an offline agent-based evaluation framework that models coordinated accounts, dynamic ranking, controlled non-bot responses, and ranking interventions in a shared closed-loop process. CoSimRec introduces the Algorithmic Penetration Rate (APR) metric family: exposure APR is the primary endpoint, while behavior APR is a response-model-conditional sensitivity measure; both can be compared with matched no-attack baselines. We evaluate CoSimRec on MIND, MovieLens, and LastFM with random, popularity-based, feedback-sensitive, MF, BPR-MF, and BPR-LightGCN recommenders. In a risk-blind primary protocol, random controls show no statistically supported positive penetration, whereas popularity-based and feedback-sensitive ranking produce positive APR-Lift in all six master-worker settings, reaching 0.4702 on LastFM. A nine-target MovieLens 1M LightGCN stress test shows positive mean APR-Lift around 25\% injection in all three target-popularity strata, while no-filler profiles remain near zero. Under these controlled conditions, coordinated inputs reach non-bot recommendation slots, providing evidence of a computational pathway from organized activity to audience-level visibility.
A large literature uses exogenous variation to estimate how assignment to classrooms or other groups shapes social networks. Yet most of these analyses remain dyadic, treating each link in isolation, even though ties often form through triadic closure, as a friend of a friend also becomes a friend. Using fine-grained data on phone calls, text messages, physical co-location, and social-media ties, we estimate the network formation effects of randomly assigning first-year university students to classrooms and to smaller social groups. To analyze explicitly whether group assignment interact with triadic closure, we use our random assignment to estimate a subgraph generated model of network formation. Accounting for triadic closure turns out to be crucial. For social groups in particular, group assignment affects network formation almost entirely by inducing additional triadic closure. Estimates ignoring triadic closure can thus yield misleading predictions about the network effects and benefits of group assignment policies.
Wisdom of crowds estimates can be compromised when some agents’ predictions are systematically biased. A natural remedy is to aggregate predictions from a subset of more accurate agents rather than the entire crowd. I propose cluster weight on advice (CWOA), a novel “two-shot” algorithm to identify a more accurate subgroup in a single-prediction-problem context. CWOA first applies kernel density estimation to identify clusters of similar initial predictions. If multiple clusters emerge—indicating potential heterogeneity in agents’ information—the algorithm proceeds to present a piece of numerical advice (e.g., the group mean) and elicit updated predictions. This enables the calculation of the weight on advice (WOA)—the scaled magnitude of each agent’s belief revision. CWOA then averages the updated predictions within the cluster with the lowest mean WOA. A behavioral model and simulations explain both why and when cluster-level WOA signals accuracy. Better-informed agents—having already incorporated higher-quality information—perceive less corrective value in the advice and therefore, exhibit lower WOA. CWOA does not require agents to know the true biases or the composition of the crowd; a modest relative advantage in perceived estimation bias by better-informed agents may be sufficient, even under misperceptions of variance, advice quality, and psychological biases in advice taking. Empirically, I first test and confirm the model’s key insight in a controlled experimental setting. I then validate CWOA’s performance across multiple preregistered and archival data sets, including a study in which numerical advice comes from artificial intelligence. CWOA consistently outperforms benchmarks, including the state-of-the-art metaprediction-based methods. This paper was accepted by Jack Soll, behavioral economics and decision analysis. Supplemental Material: The online appendix and data files are available at https://doi.org/10.1287/mnsc.2024.04499 .
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