Prior research has used an Interactive Landslide Simulator tool to investigate human decision making against landslide risks. It has been found that repeated feedback in the ILS tool about damages due to landslides causes an improvement in human decisions against landslide risks. However, little is known on how theories of learning from feedback would account for human decisions in the ILS tool. The primary goal of this paper is to account for human decisions in the ILS tool via computational models (...) based upon reinforcement learning and to explore the model mechanisms involved when people make decisions in the ILS tool. Four different reinforcement-learning models were developed and evaluated in their ability to capture human decisions in an experiment involving two conditions in the ILS tool. The parameters of an Expectancy-Valence model, two Prospect-Valence-Learning models, a combination EV-PU model, and a random model were calibrated to human decisions in the ILS tool across the two conditions. Later, different models with their calibrated parameters were generalized to data collected in an experiment involving a new condition in ILS. When generalized to this new condition, the PVL-2 model’s parameters of both damage-feedback conditions outperformed all other RL models. We highlight the implications of our results for decision making against landslide risks. (shrink)

In this article, we examine the advantages of simple metacognitive capabilities in a repeated social dilemma. Two types of metacognitive agent were developed and compared with a non-metacognitive agent and two fixed-strategy agents. The first type of metacognitive agent takes the perspective of the opponent to anticipate the opponent's future actions and respond accordingly. The other metacognitive agent predicts the opponent's next move based on the previous moves of the agent and the opponent. The modeler agent achieves better individual outcomes (...) than a non-metacognitive agent and is more successful at encouraging cooperation. The opponent-perspective agent, by contrast, fails to achieve these outcomes because it lacks important information about the opponent. These simple agents provide insights regarding modeling of metacognition in more complex tasks. (shrink)

How do people use information from others to solve complex problems? Prior work has addressed this question by placing people in social learning situations where the problems they were asked to solve required varying degrees of exploration. This past work uncovered important interactions between groups' connectivity and the problem's complexity: the advantage of less connected networks over more connected networks increased as exploration was increasingly required for optimally solving the problem at hand. We propose the Social Interpolation Model (SIM), an (...) agent‐based model to explore the cognitive mechanisms that can underlie exploratory behavior in groups. Through results from simulation experiments, we conclude that “exploration” may not be a single cognitive property, but rather the emergent result of three distinct behavioral and cognitive mechanisms, namely, (a) breadth of generalization, (b) quality of prior expectation, and (c) relative valuation of self‐obtained information. We formalize these mechanisms in the SIM, and explore their effects on group dynamics and success at solving different kinds of problems. Our main finding is that broad generalization and high quality of prior expectation facilitate successful search in environments where exploration is important, and hinder successful search in environments where exploitation alone is sufficient. (shrink)

We don't yet have adequate theories of what the human mind is representing when it represents a social group. Worse still, many people think we do. This mistaken belief is a consequence of the state of play: Until now, researchers have relied on their own intuitions to link up the concept social group on the one hand and the results of particular studies or models on the other. While necessary, this reliance on intuition has been purchased at a considerable cost. (...) When looked at soberly, existing theories of social groups are either literal, but not remotely adequate, or simply metaphorical. Intuition is filling in the gaps of an explicit theory. This paper presents a computational theory of what, literally, a group representation is in the context of conflict: It is the assignment of agents to specific roles within a small number of triadic interaction types. This “mental definition” of a group paves the way for a computational theory of social groups – in that it provides a theory of what exactly the information-processing problem of representing and reasoning about a group is. For psychologists, this paper offers a different way to conceptualize and study groups, and suggests that a non-tautological definition of a social group is possible. For cognitive scientists, this paper provides a computational benchmark against which natural and artificial intelligences can be held. (shrink)

Behaviour change via monetary investments is a way to fighting climate change. Prior research has investigated the role of climate-change investments using a Collective-Risk-Social-Dilemma game, where players have to collectively reach a target by contributing to a climate fund; failing which they lose their investments with a probability. However, little is known on how variability in the availability of information about players’ investments influences investment decisions in CRSD. In an experiment involving CRSD, 480 participants were randomly assigned to different conditions (...) that differed in the availability of investment information among players. Half of the players possessed a higher starting endowment compared to other players. Results revealed that investments against climate change were higher when investment information was available to all players compared to when this information was available only to a few players or to no one. Similarly, investments were higher among rich players compared to poor players when information was available among all players compared to when it was available only to a few players or to no one. Again, the average investment was significantly greater compared to the Nash investment when investment information was available to all players compared to when this information was available only to a few players or to no one. We highlight some implications of our laboratory experiment for human decision-making against climate change. (shrink)

Topics in Cognitive Science, Volume 14, Issue 1, Page 14-30, January 2022.