The Determinants of Response Time in a Repeated Constant-Sum Game: A Robust Bayesian Hierarchical Model

Spiliopoulos, Leonidas

doi:10.2139/ssrn.2740989

Cited by 6 publications

(6 citation statements)

References 115 publications

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“…The model's RT predictions, for both erroneous and correct decisions conditional on the degree of conflict or agreement of the two processes, were empirically verified in a belief-updating experiment. Spiliopoulos (2016) similarly validates the model's qualitative RT predictions in repeated games, where the automatic process is specified as the win-stay/lose-shift heuristic and the controlled process as the pattern-detecting reinforcement learning model introduced in Spiliopoulos (2013). Conflict between the two processes led to longer RT, and also influenced RTs conditional on the interaction between conflict and which process the chosen action was consistent with.…”

Section: Dual-system Modelssupporting

confidence: 70%

“…Another common solution is to appropriately transform the RT distribution into an approximate normal distribution, usually through the use of a log-transform. Outliers can have a significant impact on parametric summary statistics; possible solutions include using (a) robust non-parametric statistics, (b) Student t-distributions that allow for fat-tailed distributions (e.g., Spiliopoulos 2016), and (c) hierarchical modeling (see Sect. 6.3).…”

Section: Irregular Rt Distributions Outliers and Non-responsesmentioning

confidence: 99%

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The BCD of Response Time Analysis in Experimental Economics

Spiliopoulos

Ortmann²

2014

SSRN Journal

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For decisions in the wild, time is of the essence. Available decision time is often cut short through natural or artificial constraints, or is impinged upon by the opportunity cost of time. Experimental economists have only recently begun to conduct experiments with time constraints and to analyze response time (RT) data, in contrast to experimental psychologists. RT analysis has proven valuable for the identification of individual and strategic decision processes including identification of social preferences in the latter case, model comparison/selection, and the investigation of heuristics that combine speed and performance by exploiting environmental regularities. Here we focus on the benefits, challenges, and desiderata of RT analysis in strategic decision making. We argue that unlocking the potential of RT analysis requires the adoption of process-based models instead of outcomebased models, and discuss how RT in the wild can be captured by time-constrained experiments in the lab. We conclude that RT analysis holds considerable potential for experimental economics, deserves greater attention as a methodological tool, and promises important insights on strategic decision making in naturally occurring environments. Keywords Response time Á Time constraints Á Experimental economics Á Procedural rationality Á Games Á Strategic decision making BCD refers to Benefits, Challenges, and Desiderata.

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Section: Dual-system Modelssupporting

confidence: 70%

Section: Irregular Rt Distributions Outliers and Non-responsesmentioning

confidence: 99%

The BCD of Response Time Analysis in Experimental Economics

Spiliopoulos

Ortmann²

2014

SSRN Journal

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“…Nishi et al (2016) and Nishi et al (2017) find that in repeated social dilemmas response times correlate with the level of previous group cooperation. Spiliopoulos (2016) studies a repeated 2 × 2 constant-sum game and finds that the winstay-lose-shift heuristic is associated with faster choices. Brañas-Garza et al (forthcoming) study the response times of ultimatum-game proposers.…”

Section: Introductionmentioning

confidence: 99%

Strategic Complexity and the Value of Thinking

Gill¹,

Prowse

2022

SSRN Journal

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Section: Introductionmentioning

confidence: 99%

Strategic Complexity and the Value of Thinking

Gill¹,

Prowse

2017

SSRN Journal

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Response times are a simple low-cost indicator of the process of reasoning in strategic games. In this paper, we leverage the dynamic nature of response-time data from repeated strategic interactions to measure the strategic complexity of a situation by how long people think on average when they face that situation (where we categorize situations according to the characteristics of play in the previous round). We find that strategic complexity varies significantly across situations, and we find considerable heterogeneity in how responsive subjects' thinking times are to complexity. We also study how variation in response times at the individual level across rounds affects strategic behavior and success. We find that 'overthinking' is detrimental to performance: when a subject thinks for longer than she would normally do in a particular situation, she wins less frequently and earns less. The behavioral mechanism that drives the reduction in performance is a tendency to move away from Nash equilibrium behavior. Overthinking is detrimental even though subjects who think for longer on average tend to be more successful. Finally, cognitive ability and personality have no effect on average response times.

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The Determinants of Response Time in a Repeated Constant-Sum Game: A Robust Bayesian Hierarchical Model

Cited by 6 publications

References 115 publications

The BCD of Response Time Analysis in Experimental Economics

The BCD of Response Time Analysis in Experimental Economics

Strategic Complexity and the Value of Thinking

Strategic Complexity and the Value of Thinking

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