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on Sports and Economics |
| By: | Scott R. Baker; Justin Balthrop; Mark J. Johnson; Jason D. Kotter; Kevin Pisciotta |
| Abstract: | This paper examines the rapid expansion and convergence of retail betting markets. We analyze market design elements, discuss economic utility, and highlight shared behavioral drivers of sports betting markets, prediction markets, and retail options trading. Our review underscores how technological innovation, behavioral biases, and regulatory arbitrage have shaped recent market evolution. We highlight important considerations for policy-makers facing a changing landscape and outline possibilities for further research. |
| JEL: | G11 G4 G5 G50 K20 |
| Date: | 2026–07 |
| URL: | https://d.repec.org/n?u=RePEc:nbr:nberwo:35520 |
| By: | Amrote Seyoum Getu (TU Berlin); Radosveta Ivanova-Stenzel (TU Berlin); Michel Tolksdorf (TU Berlin); Eva Wiese (TU Berlin) |
| Abstract: | We study how perceived teammate identity shapes effort allocation in hybrid human–artificial agent teams in collaborative environments. In a real-effort online experiment and under different incentive schemes, fixed payment and team competition, participants work in a team where each team member is responsible for their own segment but can observe and intervene in teammates’ segments. Teammates are algorithmically controlled agents, framed as either artificial or human-like, with their actual behavior held constant across treatments. Participants exert approximately one-third less compensatory effort when teammates are presented with human-like cues than when they are transparently labeled as artificial, and this difference is robust across both incentive schemes. The pattern is consistent with a model in which perceived human identity reduces the non-monetary benefit of helping. The result highlights that in hybrid teams, the labeling and presentation of artificial collaborators have economically meaningful consequences for cooperative effort and the organization of team production. |
| Keywords: | human–ai collaboration; online experiment; team incentives; teamwork; tournaments; |
| JEL: | C91 D29 D83 D89 |
| Date: | 2026–07–21 |
| URL: | https://d.repec.org/n?u=RePEc:rco:dpaper:580 |
| By: | Arfa, Fatemeh Hedieh |
| Abstract: | Adaptive reuse of heritage buildings requires stakeholders, and more specifically, architects to navigate interdependent questions of heritage significance, contemporary function, environmental performance, community benefit, financial feasibility, and approval of other stakeholders (developers, investors, and regulators). Process models can structure these questions, yet conventional validation methods reveal only part of how practitioners move between them under constraint. This paper presents the design and pilot testing of Architect's Design Challenge, a tabletop serious-game prototype developed to explore professional decision-making in adaptive reuse and to examine the practical legibility of the Effective Adaptive Reuse of Heritage Buildings (EARHB) process model. Players progress through six stations, allocate limited resources, select action cards, respond to stakeholder requirements, and construct a design proposition using four predefined project-value categories represented by coloured components. Two formative test rounds, involving students and architects, were organised around introduction, gameplay, and debriefing. The tests indicated that the prototype could stimulate explicit discussion of priorities, sequencing, resource allocation, and trade-offs. They also revealed a central design tension: abstraction supported strategic play but sometimes weakened correspondence with professional practice, particularly for expert players. The study contributes a transparent mapping between research constructs and game mechanics, identifies requirements for future validation, and shows how serious gaming could make selected moments of value recognition, translation into design choices, negotiation, and revision observable within a simulated decision pathway. The evidence is exploratory. It does not establish learning effects, behavioural validity, or transfer to real projects. |
| Date: | 2026–08–27 |
| URL: | https://d.repec.org/n?u=RePEc:osf:socarx:vp6a2_v1 |
| By: | Tim Lindner; Rui Jorge Almeida; Nalan Ba\c{s}t\"urk; Stephan Smeekes |
| Abstract: | Formula One outcomes reflect the joint contributions of drivers and constructors, but these contributions are unobserved and vary over time. We propose a Bayesian state-space model that disentangles dynamic driver and constructor abilities using two observed outcomes: fastest qualifying lap times and race rankings. Both outcomes depend jointly on latent driver and constructor states that evolve at the Grand Prix level, while the race equation additionally accounts for starting-grid position. The decomposition is supported by constraints that center the driver and constructor abilities at zero, together with variation in driver-constructor assignments over time. Bayesian inference is performed using the No-U-Turn sampler under weakly informative priors that treat driver and constructor abilities symmetrically. Applying the model to the Formula One hybrid era from 2014 to 2021, we find substantial heterogeneity in both driver and constructor abilities. Driver abilities are generally more stable over time, whereas constructor abilities exhibit greater variation and, for many driver--constructor combinations, contribute more strongly to observed performance. |
| Date: | 2026–08 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2608.04629 |
| By: | Dernat, Sylvain (INRAE); Fiorio, Clara; Grillot, Myriam; Martel, Gilles; Terrier-Gesbert, Médulline |
| Abstract: | Background: Game-based interventions (GBI) increasingly embed simulation games within broader transformation processes targeting sustainability challenges. However, evaluation frameworks remain predominantly session-centred and learning-oriented, offering limited insight into how in-game dynamics translate into real-world practice change. This gap is particularly critical for GBI seeking to influence behaviours, coordination mechanisms, and institutional trajectories over extended time horizons. Method: This article proposes a framework for Assessing Game-based Interventions in Real-world contexts (AGIR), a theory-based tool designed to assess multi-level change processes in GBI. Drawing on program evaluation theory, behavioural sciences, and simulation and gaming research, AGIR structures evaluation across six levels (L0–L5): from game design and in-session dynamics (L0–L1), through learning (L2) and behavioural change (L3), to practice change (L4) and transformative systemic impacts (L5). The framework explicitly models mechanisms, contextual factors, and temporal pathways linking intervention design to sustainability outcomes. Contribution: AGIR’s contribution is threefold: it reinforces theoretical foundations by explicitly connecting design choices with evaluation concepts; it provides a comprehensive reference point enabling comparison across contexts through standardised levels and hypothesised mechanisms; and it functions as a formative accompaniment tool supporting ex ante hypothesis formulation, in itinere adjustments, and ex post impact analysis. By shifting evaluation from end-point outcome measurement toward process-oriented understanding of contribution, AGIR addresses longstanding calls for rigorous yet practical frameworks adapted to complex, action-oriented interventions. Conclusion: The framework is designed for adaptability across intervention contexts while preserving theoretical coherence. Though resource-intensive, AGIR offers practitioners and researchers a structured approach to assess whether and how GBI contribute to sustainable change, fostering an evaluation culture essential for demonstrating the impact potential of simulation games. |
| Date: | 2026–08–25 |
| URL: | https://d.repec.org/n?u=RePEc:osf:socarx:jxutk_v1 |