We analyze the dynamics of repeated interaction of two players in the Prisoner's Dilemma under various levels of interdependency information and propose an instance-based learning cognitive model to explain how cooperation emerges over time. Six hypotheses are tested regarding how a player accounts for an opponent's outcomes: the selfish hypothesis suggests ignoring information about the opponent and utilizing only the player's own outcomes; the extreme fairness hypothesis weighs the player's own and the opponent's outcomes equally; the moderate fairness hypothesis weighs (...) the opponent's outcomes less than the player's own outcomes to various extents; the linear increasing hypothesis increasingly weighs the opponent's outcomes at a constant rate with repeated interactions; the hyperbolic discounting hypothesis increasingly and nonlinearly weighs the opponent's outcomes over time; and the dynamic expectations hypothesis dynamically adjusts the weight a player gives to the opponent's outcomes, according to the gap between the expected and the actual outcomes in each interaction. When players lack explicit feedback about their opponent's choices and outcomes, results are consistent with the selfish hypothesis; however, when this information is made explicit, the best predictions result from the dynamic expectations hypothesis. (shrink)

The majority of people show persistent poor performance in reasoning about “stock-flow problems” in the laboratory. An important example is the failure to understand the relationship between the “stock” of CO2 in the atmosphere, the “inflow” via anthropogenic CO2 emissions, and the “outflow” via natural CO2 absorption. This study addresses potential causes of reasoning failures in the CO2 accumulation problem and reports two experiments involving a simple re-framing of the task as managing an analogous financial budget. In Experiment 1 a (...) financial version of the task that required participants to think in terms of controlling debt demonstrated significant improvements compared to a standard CO2 accumulation problem. Experiment 2, in which participants were invited to think about managing savings, suggested that this improvement was fortuitous and coincidental rather than due to a fundamental change in understanding the stock-flow relationships. The role of graphical information in aiding or abetting stock-flow reasoning was also explored in both experiments, with the results suggesting that graphs do not always assist understanding. The potential for leveraging the kind of reasoning exhibited in such tasks in an effort to change people's willingness to reduce CO2 emissions is briefly discussed. (shrink)

A major challenge for research in artificial intelligence is to develop systems that can infer the goals, beliefs, and intentions of others (i.e., systems that have theory of mind, ToM). In this research, we propose a cognitive ToM framework that uses a well-known theory of decisions from experience to construct a computational representation of ToM. Instance-based learning theory (IBLT) is used to construct a cognitive model that generates ToM from the observation of other agents' behavior. The IBL model of the (...) observer distinguishes itself from previous models of ToM that make unreasonable assumptions about human cognition, are hand-crafted for particular settings, complex, or unable to explain a cognitive development of ToM compared to human's ToM. The IBL model learns from the observation of goal-directed agents' behavior in a gridworld navigation task, and it infers and predicts the behaviors of the agents in new gridworlds across different degrees of decision complexity in similar ways to the way human observers do. We provide evidence for the alignment of the IBL observer's predictions under various levels of decision complexity. We also advance the demonstration of the IBL predictions using a classic test of false beliefs (the Sally–Anne test), which is commonly used to test ToM in humans. We discuss our results and the potential of the IBL observer model to improve human–machine interactions. (shrink)

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

Stocks and flows are building blocks of dynamic systems: Stocks change through inflows and outflows, such as our bank balance changing with withdrawals and deposits, or atmospheric CO2 with absorptions and emissions. However, people make systematic errors when trying to infer the behavior of dynamic systems, termed SF failure, whose cognitive explanations are yet unknown. We argue that SF failure appears when people focus on specific system elements, rather than on the system structure and gestalt. Using a standard SF task, (...) SF failure decreased by a global as opposed to local task format; individual global as opposed to local processing styles; and global as opposed to local perceptual priming. These results converge toward local processing as an explanation for SF failure. We discuss theoretical and practical implications on the connections between the scope of attention and understanding of dynamic systems. (shrink)

A framing bias shows risk aversion in problems framed as “gains” and risk seeking in problems framed as “losses,” even when these are objectively equivalent and probabilities and outcomes values are explicitly provided. We test this framing bias in situations where decision makers rely on their own experience, sampling the problem's options and seeing the outcomes before making a choice. In Experiment 1, we replicate the framing bias in description-based decisions and find risk indifference in gains and losses in experience-based (...) decisions. Predictions of an Instance-Based Learning model suggest that objective probabilities as well as the number of samples taken are factors that contribute to the lack of framing effect. We test these two factors in Experiment 2 and find no framing effect when a few samples are taken but when large samples are taken, the framing effect appears regardless of the objective probability values. Implications of behavioral results and cognitive modeling are discussed. (shrink)

Quantum probability (QP) theory provides an alternative account of empirical phenomena in decision making that classical probability (CP) theory cannot explain. Cognitive architectures combine probabilistic mechanisms with symbolic knowledge-based representations (e.g., heuristics) to address effects that motivate QP. They provide simple and natural explanations of these phenomena based on general cognitive processes such as memory retrieval, similarity-based partial matching, and associative learning.