Monte Carlo simulations were performed to analyze the degree to which two-, three- and four-step learning histories of losses and gains correlated with escalation and persistence in extended extinction (continuous loss) conditions. Simulated learning histories were randomly generated at varying lengths and compositions and warranted probabilities were determined using Bayesian Updating methods. Bayesian Updating predicted instances where particular learning sequences were more likely to engender escalation and persistence under extinction conditions. All simulations revealed greater rates of escalation and persistence in the presence of heterogeneous (e.g., both Wins and Losses) lag sequences, with substantially increased rates of escalation when lags comprised predominantly of losses were followed by wins. These methods were then applied to human investment choices in earlier experiments. The Bayesian Updating models corresponded with data obtained from these experiments. These findings suggest that Bayesian Updating can be utilized as a model for understanding how and when individual commitment may escalate and persist despite continued failures.


Data were simulated using CSPRNG based on the ISAAC algorithm.  Each of the Bayesian Updating models were evaluated through 100,000 simulated Bayesian Agents.  Simulation data were well-distributed and good variability was observed throughout the simulation.


All data sets from simulations are shared and available for review.  Hosting is provided by www.smallnstats.com.  All CSV output is available here [SHA-1: c137dce23fdc0fc4c756bf1215bc76021f7e3b08].


An online implementation of the Bayesian Agent and Bayesian Updating has also been developed by the authors.  This resource will simulate a Bayesian Agent and apply all three Bayesian models to the data simulated.  This can be viewed at bayesianescalation.appspot.com.