Decades of research has demonstrated that people do not make economically rational decisions when risk is involved. While research has focused on how and why people’s decisions differ from economic rationality, an unanswered question is whether people can learn to improve their risky decision making in response to feedback on similar past decisions. In an experiment, we test whether people’s risk preferences change after they received either natural (a single outcome), or idealised (the average outcome) feedback about similar decisions in the past. Crucially, unlike studies of decisions from experience, they received feedback on several different decisions from description, rather than repeated feedback about a single choice. Compared to a control, who received no feedback, both forms of feedback led to an increase in choice of the higher Expected Value gamble. Modelling suggests, for natural feedback, this may be due to changes in how they treat probability information.

One frequent piece of advice is not to “put all our eggs in one basket” and opt for multiple alternatives in order to minimize risk and uncertainty in our decisions. In a behavioral study involving decisions-from-experience, Ashby, Konstantinidis, and Gonzalez (2015) showed that participants follow an “irrational” strategy in choice selection which departs from maximization. As structural complexity (number of available options) increased, participants diversified their choices more, proportional to rank ordering options based on their expected value. The current work explores the underlying cognitive mechanisms through a reinforcement-learning model and shows that people’s choices can be explained by a singular strategy (diversification in choice), which originates from similar cognitive processes regardless of structural complexity

Contextual Multi-Armed Bandit (CMAB) tasks are a novel framework to assess decision making in uncertain environments. In a CMAB task, participants are presented with multiple options (arms) which are characterized by a number of features (context) related to the reward as- sociated with the arms. By choosing arms repeatedly and observing the reward, participants can learn about the relation between context and reward and improve their decision strategy. We present two studies on how people behave in CMAB tasks. Within a stationary en- vironment, we ?nd that participants are best described by Thompson Sampling-based Gaussian Process mod- els. This decision rule incorporates probability match- ing to the expected outcomes derived from a rational model of the task and it is especially well-adapted to non-stationary environments. In a dynamic CMAB task we again ?nd that participants are best described by probability matching of Gaussian Process expectations. Our ?ndings imply that behavior previously referred to as \irrational" can actually be seen as a well-adapted strategy based on powerful inference algorithms. Keywords: Decision Making, Learning, Exploration- Exploitation, Contextual Multi-Armed Bandits

Warning labels can be considered as descriptions added to repeated decisions-from-experience. Limited researchso far has looked at the theoretical integration of decisions from descriptions and decisions from experience when the two areavailable concurrently. We explore how the presence and timing of such warning labels influence behaviour. We expected theprovision of warning labels to subsequently reduce risk taking, and that more prior experience before the appearance of suchlabels would lead to stronger habit formation and reduce their behavioural impact. Instead, we show how the appearance ofdescriptions warning against risks can have a perverse effect of increasing risk taking. And counter-intuitively, we also observethat the amount of previous experience prior to the appearance of descriptions does not impact behaviour. Briefly presentedwarning labels also have the same effect as constantly present ones. All of these findings have strong implications on the designof effective warning labels.

People solve a myriad of coordination problems without explicit communication every day. A recent theoretical account, virtual bargaining, proposes that, to coordinate, we often simulate a negotiation process, and act according to what we would be most likely to agree to do if we were to bargain. But very often several equivalent tacit agreements — or virtual bargains — are available, which poses the challenge of figuring out which one to follow. Here we take inspiration from virtual bargaining to develop a cognitive modeling framework for dynamic coordination problems. We assume that players recognize their common goal, identify one or more possible tacit agreements based on situational features, observe the history of their partner's choices to infer the most likely tacit agreement, and play their role in the joint plan. We test this approach in two experiments (n = 125 and n = 133) based on a dynamic coordination game designed to elicit agreement-based behavior. We fit our model at the individual level and compare its performance against alternative models. Across four different conditions, our model performs best among the set of models considered. Behavioral results are also consistent with players sustaining coordination and cooperation in the task by converging on tacitly agreed strategies or “virtual bargains”.

Contractualist moral theories hold that morality is primarily about acting according to what would be agreed by rational agents. The contractualist tradition has received little attention in empirical work compared to competing normative ethical theories that are primarily concerned with rules (deontology) and consequences (consequentialism). Recent theoretical developments and empirical results suggest that agreement-based considerations and forms of reasoning could play a central role in moral judgment and decision making. However, the computational foundations of moral contractualism have scarcely been explored. In this paper, we present a flexible experimental paradigm to study contractualist moral behavior in an incentivized setting. We then develop a simple computational model of agreement-based moral decision making that captures participants’ behavior in the task. Results suggest that agreement-based cognitive processes are required to explain the nuances of observed behavioral patterns. Contractualism could offer a fruitful framework to understand how humans coordinate their behavior in morally ambiguous contexts.

A constant element of our modern environment is change. In decision-making research however, very little is known about how people make choices in dynamic environments. We report the results of an experiment where participants were asked to choose between two options: a dynamic and risky option that resulted in either a high or a low outcome, and a stationary and safe option that resulted in a medium outcome. The probability of the high outcome in the risky option decreased or increased linearly over the course of the task while the probability of the medium outcome stayed the same throughout. We find that adaptation to change is related to the direction of that change, and that the way people adapt to changing probabilities relates to their willingness to explore available options. A cognitive model based on Instance-Based Learning Theory reproduces the behavioral patterns.

Previous research on the effects of probability and delay ondecision-making has focused on examining each dimensionseparately, and hence little is known about when these dimen-sions are combined into a single choice option. Importantly,we know little about the psychological processes underlyingchoice behavior with rewards that are both delayed and proba-bilistic. Using a process-tracing experimental design, we mon-itored information acquisition patterns and processing strate-gies. We found that probability and delay are processed se-quentially and evaluations of risky delayed prospects are de-pendent on the sequence of information acquisition. Amongchoice strategies, directly comparing the values of each dimen-sion (i.e., dimension-wise processing) appears to be most fa-vored by participants. Our results provide insights into the psy-chological plausibility of existing computational models andmake suggestions for the development of a process model forrisky intertemporal choice.