What characters believe, how they act based on those beliefs,and how their beliefs are updated is an essential element of many stories. State-space narrative planning algorithms treat their search spaces like a set of temporally possible worlds. We present an extension that models character beliefs as epistemically possible worlds and describe how such a space is generated. We also present the results of an experiment that demonstrates that the model meets the expectations of a human audience.

It is a truism that the knowledge of groups of people, particularly experts, outperforms that of individuals [43] and there is increasing call to use the dispersed judgments of the crowd in policy making [42]. There is a large literature spanning multiple disciplines on methods for aggregating beliefs (for reviews see [9, 6, 7]), and previous applications have included political and economic forecasting [3, 27], evaluating nuclear safety [10] and public policy [28], and assessing the quality of chemical probes [31]. However, previous approaches to aggregating beliefs have implicitly assumed'kind' (as opposed to'wicked') environments [16]. In a previous paper, [35] we proposed an algorithm for aggregating beliefs using not only respondent's answers but also their prediction of the answer distribution, and proved that for an infinite number of non-noisy Bayesian respondents, it would always determine the correct answer if sufficient evidence was available in the world. 1 Here, we build on this approach but treat the aggregation problem as one of statistical inference. We propose a model of how people formulate their own judgments and predict the distribution of the judgments of others, and use this model to infer the most probable world state giving rise to the observed data from people. The model can be applied at the level of a single question but also across multiple questions, to infer the domain expertise of respondents. The model is thus broader in scope than other machine learning models for aggregation in that it accepts unique questions, but can also be compared to their performance across multiple questions. We do not assume that the aggregation model has access to correct answers or to historical data about the performance of respondents on similar questions. By using a simple model of how people make such judgments, we are able to increase the accuracy of the group's aggregate answer in domains ranging from estimating art prices to diagnosing skin lesions.