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on Sports and Economics |
| By: | Campbell, James |
| Abstract: | A video game developer can monetize its content (maps, themes, modes) in two ways: upfront pricing, a single high up-front price for the game with all content included, or in-game purchases, a free or cheap base with each content piece sold separately. We model this as a choice between bundling and separately selling a content stream and ask when in-game purchases dominate. Upfront pricing wins when content tastes are dispersed and weakly correlated; in-game purchases win when tastes are strongly correlated or pieces differ in appeal, when risk-averse players face uncertain content, and when players are present-biased or impulsive. A simulation with all forces active shows that bundling is the benchmark optimum and that each friction shifts the boundary toward in-game purchases, with behavioral forces moving it most. The framework explains why premium console and PC games are still sold up front, while long-lived games, especially mobile games, rely on in-game purchases. |
| Date: | 2026–06–16 |
| URL: | https://d.repec.org/n?u=RePEc:osf:socarx:c7ebt_v1 |
| By: | Braghieri, Luca; Bursztyn, Leonardo; Fasnacht, Jan |
| Abstract: | Voting-based collective decisions are typically made either anonymously or publicly. Anonymous voting protects truthful expression but conceals individual behavior; public voting provides information about individual votes, but, when one option is socially stigmatized, it can distort participation and choices. We introduce threshold majority voting, in which voters choose a disclosure threshold determining whether and when their votes are revealed. In an experiment at UC Berkeley on the participation of transgender women in women’s sports, public voting nearly doubles abstention and reduces support for the stigmatized option. Threshold voting eliminates these distortions while revealing one-third of individual votes. |
| JEL: | D72 D82 C93 |
| Date: | 2026–05 |
| URL: | https://d.repec.org/n?u=RePEc:cpr:ceprdp:21485 |
| By: | Ade Adegbenro |
| Abstract: | Prediction markets are usually evaluated after their contracts exist, by asking how well prices forecast outcomes. We study the prior institutional margin of market formation, asking which uncertainties become tradable contracts at all. Using an audited dataset of 6, 047 Africa-topic and Latin America-topic contracts listed on Polymarket and Kalshi, we construct a coded measure of settlement legibility, the degree to which an uncertainty can be worded, sourced, and credibly resolved by third parties, and validate it on 451 units under a frozen codebook, where independent double scoring reaches ordinal reliabilities of 0.92 and 0.96 on the primary dimensions and blind human benchmarks reach 0.97 and 0.92. Using this measure, we find that formation is selective in ways that public importance does not explain, with African inventory concentrated overwhelmingly in football while salient civic events produce little or no inventory, and Latin American inventory deeper but dominated by Venezuela, where attention to prospective United States military action sustains the largest civic cluster in the data. Legibility orders the inventory steeply, with sports and elections near the top of the scale and conflict at the bottom. In a formation test against an externally assembled frame of 131 civic events, legibility predicts listing in the expected direction but falls short of pre-specified acceptance criteria, while among listed contracts the relation between legibility and trading value is negative, as a model of selective listing implies and as we predicted before estimation. Prediction-market inventories therefore measure what platforms can settle as much as what traders believe, and reading them as maps of public interest conflates the two. |
| Date: | 2026–06 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2606.17503 |
| By: | Christian König-Kersting; Yana Litovsky; Robert Böhm; Igor Grossmann; Jürgen Huber; Michael Kirchler; WoCCAP consortium |
| Abstract: | Accurate forecasts are central to decision-making in many domains. Although aggregating independent judgments can improve forecast accuracy, a phenomenon known as “Wisdom of the Crowd, ” methods for effectively combining these forecasts remain underexplored. In this preregistered many-designs study, 129 research teams independently submitted a total of 516 algorithms to aggregate forecasts over six months, across four domains: economics, politics, climate, and sports. By testing hundreds of independently developed aggregation algorithms under identical conditions, we established an empirical benchmark for forecast aggregation. Drawing on forecasts by 1, 182 people, we evaluated algorithm accuracy and variability. The share of algorithms significantly outperforming the mean and median varied by domain, with the strongest gains over simple benchmarks in politics and climate. Algorithm performance was also more persistent over time in politics and climate than in economics and sports. When asked to predict algorithm performance, the participating researchers were overconfident about the success of their own submissions and of others. We also examined potential predictors of algorithm accuracy and variability. Although researchers expected various features to predict accuracy, no algorithmic or researcher feature consistently explained why algorithms varied in performance. The main exception was the use by algorithms of previously observed realized outcomes, typically to compute past forecast error or train a model, which predicted greater accuracy in politics and climate. The null findings contrast with prior evidence that certain weighting criteria can improve forecast aggregation, and illustrate the value of preregistered many-designs studies which report the full distribution of submitted approaches, revealing when previously supported approaches fail to generalize. |
| Keywords: | Meta-science, Forecasting, Wisdom of the Crowd |
| Date: | 2026–05 |
| URL: | https://d.repec.org/n?u=RePEc:inn:wpaper:2026-05 |