On the effectiveness of COVID-19 restrictions and lockdowns: Pan metron ariston.

BACKGROUND: Early evaluations of the effectiveness of non-pharmaceutical intervention (NPI) mandates were constrained by the lack of empirical data, thereby also limiting model sophistication (e.g., models did not take into account the endogeneity of key variables). METHODS: Observational analysis using a behavioral four-equation structural model that accounts for the endogeneity of many variables and correlated unobservable country characteristics. The dataset includes information from 132 countries from February 15, 2020, to April 14, 2021, with data on confirmed cases and deaths, mobility, vaccination and testing rates, and NPI stringency. The main outcomes of interest are the growth rates of confirmed cases and deaths. RESULTS: There were strongly decreasing returns to more stringent NPI mandates. No additional impact was found for NPI mandates beyond a Stringency Index range of 51-60 for cases and 41-50 for deaths. A nonrestrictive policy of extensive and open testing constituted 51% [27% to 76%] of the impact on pandemic dynamics of the optimal NPIs. Reductions in mobility were found to increase, not decrease, both case [Formula: see text] and death growth rates [Formula: see text]. More stringent restrictions on gatherings and international movement were found to be effective. Governments conditioned policy choices on recent pandemic dynamics, and were found to be more hesitant in de-escalating NPIs than they were in imposing them. CONCLUSION: At least 90% of the maximum effectiveness of NPI mandates is attainable with interventions associated with a Stringency Index in the range of 31-40, which impose minimal negative social externalities. This was significantly less than the average stringency level of implemented policies around the world during the same time period.

Fair play: social norm compliance failures in behavioural variant frontotemporal dementia.

Adherence to social norms is compromised in a variety of neuropsychiatric conditions. Functional neuroimaging studies have investigated social norm compliance in healthy individuals, leading to the identification of a network of fronto-subcortical regions that underpins this ability. However, there is a lack of corroborative evidence from human lesion models investigating the structural anatomy of norm compliance across this fronto-subcortical network. To address this, we developed a neuroeconomic task to investigate social norm compliance in a neurodegenerative lesion model: behavioural variant frontotemporal dementia, a condition characterized by gross social dysfunction. The task assessed norm compliance across three behaviours that are well-studied in the neuroeconomics literature: fairness, prosocial and punishing behaviours. We administered our novel version of the Ultimatum Game in 22 patients with behavioural variant frontotemporal dementia and 22 age-matched controls, to assess how decision-making behaviour was modulated in response to (i) fairness of monetary offers; and (ii) social context of monetary offers designed to produce either prosocial or punishing behaviours. Voxel-based morphometry was used to characterize patterns of grey matter atrophy associated with task performance. Acceptance rates between patients and controls were equivalent when only fairness was manipulated. However, patients were impaired in modulating their decisions in response to social contextual information. Patients' performance in the punishment condition was consistent with a reduced tendency to engage in punishment; this was associated with decreased grey matter volume in the anterior cingulate, orbitofrontal cortex, left dorsolateral prefrontal cortex and right inferior frontal gyrus. In the prosocial condition, patients' performance suggested a reduced expression of prosocial behaviour, associated with decreased grey matter in the anterior insula, lateral orbitofrontal cortex, anterior cingulate and dorsal striatum. Acceptance rates in the Ultimatum Game were also significantly related to impairments in the everyday expression of empathic concern. In conclusion, we demonstrate that compliance to basic social norms (fairness) can be maintained in behavioural variant frontotemporal dementia; however, more complex normative behaviours (prosociality, punishment) that require integration of social contextual information are disrupted in association with atrophy in key fronto-striatal regions. These results suggest that the integration of social contextual information to guide normative behaviour is uniquely impaired in behavioural variant frontotemporal dementia, and may explain other common features of the condition including gullibility and impaired empathy. Our findings also converge with previous functional neuroimaging investigations in healthy individuals and provide the first description of the structural anatomy of social norm compliance in a neurodegenerative lesion model.

Variance, skewness and multiple outcomes in described and experienced prospects: Can one descriptive model capture it all?

We determined the scope of five decision models of choices across four environmental niches defined by whether outcome probabilities are described (risk) or experienced by sampling (uncertainty) and whether lotteries are simple (one or two outcomes per prospect) or complex (three or four). The majority of participants chose in accordance with cumulative prospect theory only in simple environments involving decisions from description (75%). In complex environments involving decisions from description and experience, however, skewness-preference models were more prevalent (57% and 68%, respectively). Consequently, in niches outside of simple lotteries under risk, rank dependence and nonlinear probability weighting failed to accurately describe the majority of choices. Exploiting elicited subjective beliefs in decisions from experience, we found that experienced (sampled) outcome likelihoods outperformed elicited beliefs in predicting choices and found scant evidence for two-stage models of decisions under uncertainty. Finally, we found statistically significant evidence that 90% of participants chose as if they relied on different models across environments; nonetheless, assuming as if participants used a single model across all environments to predict out-of-sample choice only minimally reduced prediction accuracy. We discuss the implications of model mimicry and task diagnosticity in light of these results in terms of both economic and statistical significance, both for model comparisons and inference. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Studies in Ecological Rationality.

Ecological rationality represents an alternative to classic frameworks of rationality. Extending on Herbert Simon's concept of bounded rationality, it holds that cognitive processes, including simple heuristics, are not per se rational or irrational, but that their success rests on their degree of fit to relevant environmental structures. The key is therefore to understand how cognitive and environmental structures slot together. In recent years, a growing set of analyses of heuristic-environment systems has deepened the understanding of the human mind and how boundedly rational heuristics can result in successful decision making. This article is concerned with three conceptual challenges in the study of ecological rationality. First, do heuristics also succeed in dynamic contexts involving competitive agents? Second, can the mind adapt to environmental structures via an unsupervised learning process? Third, how can research go beyond mere descriptions of environmental structures to develop theories of the mechanisms that give rise to those structures? In addressing these questions, we illustrate that a successful theory of rationality will focus on the adaptive aspects of the mind and will need to account for three components: the mind's information processing, the environment to which the mind adapts, and the intersection between the environment and the mind.

A map of ecologically rational heuristics for uncertain strategic worlds.

The ecological rationality of heuristics has been extensively investigated in the domain of individual decision making. In strategic decision making, however, the focus has been on repeated games, and there is a lack of research on 1-shot games, where opponents and the game itself can vary from one interaction to another. Mapping the performance of simple versus more complex decision policies (or strategies) from the experimental game theory literature is an important first step in this direction. We investigate how 10 policies fare conditional on strategic properties of the games and 2 classes of uncertainty. The strategic properties are the complexity (number of actions) and the degree of harmony (competitiveness) of the games. The first class of uncertainty is environmental (or payoff) uncertainty, arising from missing payoff values. The second class is strategic uncertainty about the type of opponent a player is facing. Policies' performance was measured by 3 criteria: a mean criterion averaging over the whole set of opponent policies, a maxmin criterion capturing the worst-case scenario and another criterion measuring robustness to different distributions of opponent policies. Heuristics performed well and were more robust than complex policies such as pure-strategy Nash equilibria, while simultaneously requiring significantly less information and fewer computational resources. Our ranking of the decision policies' performance was closely aligned to their prevalence in experimental studies of games. In particular, the Level-1 policy, which completely ignores an opponent's payoffs and uses equal weighting to determine the expected payoffs of different actions, exhibited a robust beauty. (PsycINFO Database Record (c) 2020 APA, all rights reserved).

Nonlinear decision weights or moment-based preferences? A model competition involving described and experienced skewness.

The predictive power of cumulative prospect theory and expected utility theory is typically compared using decisions from description, where lotteries' outcome values and probabilities are explicitly stated. In decisions from experience, individuals sample (in the sampling paradigm without cost) from the return distributions to learn outcome values and their relative frequencies; here cumulative prospect theory and expected utility theory require the calculation of probabilities from experience. Individuals, however, may be more attuned to the experienced moments of outcome distributions, rather than the probabilities. We therefore test the mean-variance-skewness model, and retrieve the proportion of expected utility theory (over income), cumulative prospect theory, and mean-variance-skewness populations using a latent-class hierarchical Bayesian model across six large datasets. For simple lotteries (with 1-2 outcomes), we find a mixture of cumulative prospect theory and mean-variance-skewness populations in decisions from both description and experience. For more complex lotteries (with 2-3 outcomes), all participants are classified as cumulative prospect theory types in decisions from description, but as mean-variance-skewness types in decisions from experience. This suggests that in decisions from experience with more complex return distributions, preferences for skewness are more predictive than nonlinear probability weighting.

The determinants of response time in a repeated constant-sum game: A robust Bayesian hierarchical dual-process model.

The investigation of response time and behavior has a long tradition in cognitive psychology, particularly for non-strategic decision-making. Recently, experimental economists have also studied response time in strategic interactions, but with an emphasis on either one-shot games or repeated social-dilemmas. I investigate the determinants of response time in a repeated (pure-conflict) game, admitting a unique mixed strategy Nash equilibrium, with fixed partner matching. Response times depend upon the interaction of two decision models embedded in a dual-process framework (Achtziger and Alós-Ferrer, 2014; Alós-Ferrer, 2016). The first decision model is the commonly used win-stay/lose-shift heuristic and the second the pattern-detecting reinforcement learning model in Spiliopoulos (2013b). The former is less complex and can be executed more quickly than the latter. As predicted, conflict between these two models (i.e., each one recommending a different course of action) led to longer response times than cases without conflict. The dual-process framework makes other qualitative response time predictions arising from the interaction between the existence (or not) of conflict and which one of the two decision models the chosen action is consistent with-these were broadly verified by the data. Other determinants of RT were hypothesized on the basis of existing theory and tested empirically. Response times were strongly dependent on the actions chosen by both players in the previous rounds and the resulting outcomes. Specifically, response time was shortest after a win in the previous round where the maximum possible payoff was obtained; response time after losses was significantly longer. Strongly auto-correlated behavior (regardless of its sign) was also associated with longer response times. I conclude that, similar to other tasks, there is a strong coupling in repeated games between behavior and RT, which can be exploited to further our understanding of decision making.

The BCD of response time analysis in experimental economics.

For decisions in the wild, time is of the essence. Available decision time is often cut short through natural or artificial constraints, or is impinged upon by the opportunity cost of time. Experimental economists have only recently begun to conduct experiments with time constraints and to analyze response time (RT) data, in contrast to experimental psychologists. RT analysis has proven valuable for the identification of individual and strategic decision processes including identification of social preferences in the latter case, model comparison/selection, and the investigation of heuristics that combine speed and performance by exploiting environmental regularities. Here we focus on the benefits, challenges, and desiderata of RT analysis in strategic decision making. We argue that unlocking the potential of RT analysis requires the adoption of process-based models instead of outcome-based models, and discuss how RT in the wild can be captured by time-constrained experiments in the lab. We conclude that RT analysis holds considerable potential for experimental economics, deserves greater attention as a methodological tool, and promises important insights on strategic decision making in naturally occurring environments.

Complexity, attention, and choice in games under time constraints: A process analysis.

We test empirically the strategic counterpart of the Adaptive Decision Maker hypothesis (Payne, Bettman, & Johnson, 1993), which states that decision makers adapt their attention and decision rules to time pressure in predictable ways. For 29 normal form games, we test whether players adapt to tightening time constraints by reducing their information search and shifting to less computationally demanding heuristics, ultimately leading to systematic changes in choices. We specify process models of each decision rule, thereby allowing us to rank the rules by complexity on the basis of the number of elementary information processing units required to execute each rule. The frequency of decision rule use conditional on time constraints is estimated using Bayesian latent class modeling. Under time pressure, the subject pool displayed a systematic shift toward heuristics of less complexity. We observed an increase in nonstrategic decision rules (particularly Level 1) and rules that choose the social optimum at the expense of more complex rules including the Nash equilibrium. Also, subjects are more likely to not acquire information about others' payoffs, thus effectively transforming the games into nonstrategic situations. The subjects' adaptation to time pressure was efficient in the sense that it did not lead to significant payoff losses. (PsycINFO Database Record (c) 2018 APA, all rights reserved).

Transfer of conflict and cooperation from experienced games to new games: a connectionist model of learning.

The question of whether, and if so how, learning can be transfered from previously experienced games to novel games has recently attracted the attention of the experimental game theory literature. Existing research presumes that learning operates over actions, beliefs or decision rules. This study instead uses a connectionist approach that learns a direct mapping from game payoffs to a probability distribution over own actions. Learning is operationalized as a backpropagation rule that adjusts the weights of feedforward neural networks in the direction of increasing the probability of an agent playing a myopic best response to the last game played. One advantage of this approach is that it expands the scope of the model to any possible n × n normal-form game allowing for a comprehensive model of transfer of learning. Agents are exposed to games drawn from one of seven classes of games with significantly different strategic characteristics and then forced to play games from previously unseen classes. I find significant transfer of learning, i.e., behavior that is path-dependent, or conditional on the previously seen games. Cooperation is more pronounced in new games when agents are previously exposed to games where the incentive to cooperate is stronger than the incentive to compete, i.e., when individual incentives are aligned. Prior exposure to Prisoner's dilemma, zero-sum and discoordination games led to a significant decrease in realized payoffs for all the game classes under investigation. A distinction is made between superficial and deep transfer of learning both-the former is driven by superficial payoff similarities between games, the latter by differences in the incentive structures or strategic implications of the games. I examine whether agents learn to play the Nash equilibria of games, how they select amongst multiple equilibria, and whether they transfer Nash equilibrium behavior to unseen games. Sufficient exposure to a strategically heterogeneous set of games is found to be a necessary condition for deep learning (and transfer) across game classes. Paradoxically, superficial transfer of learning is shown to lead to better outcomes than deep transfer for a wide range of game classes. The simulation results corroborate important experimental findings with human subjects, and make several novel predictions that can be tested experimentally.

Model comparisons using tournaments: likes, "dislikes," and challenges.

We examine the value added of tournaments that compare the predictive power of behavioral models. The advantages and disadvantages of this particular method have not been systematically discussed in the literature; here we aspire to do so. We conclude that tournaments are a useful addition to our arsenal of methods and a significant step forward in the study of the predictive power of behavioral models. Such tournaments are not without limitations, however, and we identify and illustrate current limitations, propose solutions to overcome some of them, and identify a research agenda to address the remaining.