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We test empirically the strategic counterpart of the Adaptive Decision Maker hypothesis (Payne et al., 1993), which states that decision makers adapt their attention and decision rules to time pressure in predictable ways. For twenty-nine normal form games, we test whether players adapt to tightening time constraints by reducing their information s...
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... proceed by defining each decision rule below, and showing the calculations required from the perspective of the row player in the game presented in Table 4. Note that Figure 2 may also be useful as a reference as it visually shows the typical information search patterns of the decision rules. ...
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... In Experiment 2c we also sought to gather some initial evidence for our claim that Level 2 strategies are more cognitively demanding than Level 0 and implicit Level 1 strategies. A number of authors have presented evidence that more complex strategies in resolutions of social dilemmas and other games (e.g., trust games) are more cognitively demanding; and/or that certain types of cues are more demanding to process than others (Evans & Krueger, 2011;Fiedler et al., 2013;Rand et al., 2012;Spiliopoulos et al., 2018). In a similar vein, we consider that Level 2 strategies, in particular, will require more of participants' cognitive resources than Level 0 and implicit Level 1 strategies. ...
... Secondly, a common approach to investigating the cognitive complexity of strategies in the context of economic games is to measure, or limit, participants' response times, with the broad hypothesis being that participants employing more cognitively-demanding strategies will tend to be slower to make their decisions (see, e.g., Rand et al. (2012); Spiliopoulos et al. (2018), though note Evans et al. (2015) and Evans & Rand (2019), which suggest that decision conflict is what primarily drives reaction times). Being able to present responsetime analyses showing that participants employing Level 2 mindreading strategies (i.e., combining signals with payoffs and/or reliability information) tended to be slower to make their choices would thus have been useful convergent evidence for our model. ...
This paper introduces the Hierarchical Mindreading Model (HMM), a new model of mindreading in two-person, mixed-motive games such as the Prisoners’ Dilemma. The HMM proposes that the strategies available to decision makers in these games can be classified on a hierarchy according to the type of mindreading involved. At Level 0 of the HMM, there is no attempt to infer the intentions of the other player from any of the context-specific information (i.e., signals, payoffs, or partner reliability). At Level 1, decision makers rely on signals to infer the other’s intention, without considering the possibility that those signals might not reflect the other’s true intention. Finally, in Level 2 strategies, decision makers infer the other player’s intended choice by integrating information contained in their signals with the apparent reliability of the other participant and/or the game’s payoffs. The implications of the HMM were tested across four studies involving 962 participants, with results consistently indicating the presence of strategies from all three levels of the HMM’s hierarchy.
... According to an account from Payne et al. (1988), decision-makers adjust their strategies to optimize effort and accuracy in response to time pressure (Dambacher & Hübner, 2015;Gok & Atsan, 2016;Payne et al., 1988). These strategic changes include acceleration (faster processing; Spiliopoulos et al., 2018;Gok & Atsan, 2016) and filtration (increased specificity in information acquisition; Oh et al., 2016;Spiliopoulos et al., 2018;Maule et al., 2000;Gok & Atsan, 2016). Acceleration, evidenced by speeded information processing, is demonstrated here with decreased response times, and Filtration, selective information processing, is also apparent through enhanced selective attention to Flanker arrows (seen in reduced inhibition). ...
... According to an account from Payne et al. (1988), decision-makers adjust their strategies to optimize effort and accuracy in response to time pressure (Dambacher & Hübner, 2015;Gok & Atsan, 2016;Payne et al., 1988). These strategic changes include acceleration (faster processing; Spiliopoulos et al., 2018;Gok & Atsan, 2016) and filtration (increased specificity in information acquisition; Oh et al., 2016;Spiliopoulos et al., 2018;Maule et al., 2000;Gok & Atsan, 2016). Acceleration, evidenced by speeded information processing, is demonstrated here with decreased response times, and Filtration, selective information processing, is also apparent through enhanced selective attention to Flanker arrows (seen in reduced inhibition). ...
Executive function (EF) is critical to everyday life, but it can be undermined by adverse psychological states like stress and negative affect. For example, inadequate time to perform a task is a common stressor that can disrupt EF. Although the impact of actual time pressure on EF has been established, little is known about how self-generated, perceived time pressure (PTP) affects EF in the absence of objective time limits. We chose Eriksen's Flanker task as an index of cognitive inhibition, a key component of EF, and we varied the interval between successive trials, the inter-trial interval (ITI), to proxy PTP. This manipulation strongly impacted task performance: shrinking the ITI to increase PTP diminished cognitive inhibition and increased both stress and negative affect. Subsequently lengthening the ITI to decrease PTP reversed nearly all of these effects, except stress, which persisted. Multilevel linear regression modeling revealed that ITI and stress predicted inhibition, and exploratory mediation modeling suggested that stress mediates the relationship between ITI and inhibition. These findings validate perceived time pressure as an empirical stressor and demonstrate EF's sensitivity to changes in PTP.
... Given that readiness for responsible citizenship relies on rational thinking, and that overworked or emotionally burdened individuals may be more likely to engage in fast, heuristic processing rather than slow, effortful processing of continuously incoming information (cf. Gillard et al., 2009;Spiliopoulos et al., 2018), we suggest that children, adolescents, and adults are encouraged to replace unhealthy, workaholic habits with healthy work-home balance (e.g., Deloitte Consulting, 2010;Bannai and Tamakoshi, 2014;Anxo et al., 2017;Anxo and Karlsson, 2019). Therefore, unhealthy habits instilled in school-age children and adolescents through a tremendous amount of schoolwork should be limited to foster intentional deployment of cognitive resources from an early age. ...
Open access to information is now a universal phenomenon thanks to rapid technological developments across the globe. This open and universal access to information is a key value of democratic societies because, in principle, it supports well-informed decision-making on individual, local, and global matters. In practice, however, without appropriate readiness for navigation in a dynamic information landscape, such access to information can become a threat to public health, safety, and economy, as the COVID-19 pandemic has shown. In the past, this readiness was often conceptualized in terms of adequate literacy levels, but the contemporarily observed highest-ever literacy levels have not immunized our societies against the risks of misinformation. Therefore, in this Perspective, we argue that democratization of access to information endows citizens with new responsibilities, and second, these responsibilities demand readiness that cannot be reduced to mere literacy levels. In fact, this readiness builds on individual adequate literacy skills, but also requires rational thinking and awareness of own information processing. We gather evidence from developmental, educational, and cognitive psychology to show how these aspects of readiness could be improved through education interventions, and how they may be related to healthy work-home balance and self-efficacy. All these components of education are critical to responsible global citizenship and will determine the future direction of our societies.
... The presentation times of the task stimuli in these studies varied: some had time limits [8][9][10] and others did not [11,14]. This variation may have contributed to the differences in the study results, as time constraints during the decision-making process may influence the outcomes and strategies [16]. However, no studies have explored the effects of variations in stimuli presentation time on SRGP bias. ...
Mentalizing impairment is one of the core features of schizophrenia, and bias judgement of others’ gaze as self-directing is common to schizophrenia patients. In this case–control study, 30 patients with first-episode schizophrenia (FES) and 30 matched healthy controls were assigned gaze perception tasks with variable stimulus presentation times (300 ms and no time limit) to determine the presence of self-referential gaze perception (SRGP) bias. The eye movement pattern during the task were tracked and data were analysed using hidden Markov models (HMMs). The SRGP involves reporting of others’ gaze intent and was used as a measurement of explicit mentalizing process. Eye movement measurement represents automated visual attention pattern and was considered as a measurement of implicit mentalizing process. The patients with FES had significantly more SRGP bias than the controls in the 300 ms condition but not in the no-time-limit condition. Social cognitive function was related to SRGP bias in the patient group. Two distinct eye movement patterns were identified: eye-focused and nose-focused. Significant group differences in eye movement patterns in the 300 ms condition were found with more controls had eye-focused pattern. Social anxiety symptoms were related to the nose-focused pattern, positive psychotic symptoms were related to the eye-focused pattern, and depressive symptoms were related to less consistent eye movement patterns. No significant relationship was found between SRGP bias and eye movement patterns. The dissociation between explicit and implicit mentalizing processes with different cognitive and symptom dimensions associated with the two processes suggests the presence of different mechanisms.
... A decision policy is a mapping from the payos of any n × n normal form game to the set of n possible (pure) strategies or actions. Our analysis compared 10 decision policies (playing against each other): the (pure-strategy) Nash equilibrium, a baseline random policy (a uniform distribution over the n actions), and a set of eight heuristics that have been identied as commonly employed in laboratory experiments (Stahl and Wilson 1994;Stahl and Wilson 1995;Costa-Gomes, Crawford, and Broseta 2001;Costa-Gomes and Weizsäcker 2008;Sutter, Czermak, and Feri 2013;Polonio, Guida, and Coricelli 2015;Devetag, Guida, and Polonio 2016;Spiliopoulos, Ortmann, and Zhang 2018)see Table 1. ...
... The complexity Ω i of a policy (Spiliopoulos, Ortmann, and Zhang 2018) is the number of elementary information processing (EIP) units required to execute a decision policy (Payne, Bettman, and Johnson 1992;Payne, Bettman, and Johnson 1993;Newell and Simon 1972). ...
... The determination of the complexity of the decision policies below follows (Spiliopoulos, Ortmann, and Zhang 2018) with one exception that will be noted. ...
The emergence of heuristics, sophisticated behavior such as higher levels of Theory of Mind and substantive rationality in strategic situations can be studied using evolutionary game theory to jointly account for the interactions between players and their environments. Research thus far has been relatively constrained by limiting analyses to a small number of simple games and by focusing on selection operating over actions rather than rules or policies that prescribe behavior in a wide range of games. As a result, the existing literature has not adequately reconciled the significant evidence amassed in the behavioral game theory literature that people use bounded-rational strategies, rather than the normative prescription (Nash equilibrium). I systematically explore the differential costs of implementing decision policies and how evolution is influenced by key environmental characteristics. Detailed maps linking the performance of policies to 3,060 environments are presented demonstrating that the steady state distribution of policies under replicator dynamics is strongly dependent on: a) complexity costs, derived from the opportunity cost associated with the decision time to execute the policies at a process-level and b) the environmental characteristics such as the size of the game, the degree of payoff uncertainty and the degree of mutual interests or conflict between players. Evolution rarely selects for substantive rationality (Nash equilibrium), favoring instead a plethora of bounded-rational policies with varying levels of theory of mind (Level-k models), and in some environmental niches other heuristics such as maxmin or maximization of joint payoffs to players. An intriguing finding is that there exist non-trivial environmental niches with moderate to high complexity costs, where 1 random behavior can be selected for in conjunction with a policy that best-responds to randomness (Level-1). I conclude that while bounded-rational policies do not conform to the standards of substantive rationality, they exhibit both procedural and ecological rationality once the complexity of decision processes and interactions with environmental characteristics are accounted for.
... According to an account from Payne et al. (1988), decision-makers adjust their strategies to optimize effort and accuracy in response to time pressure (Dambacher & Hübner, 2015;Gok & Atsan, 2016;Payne et al., 1988). These strategic changes include acceleration (faster processing; Spiliopoulos et al., 2018;Gok & Atsan, 2016) and filtration (increased specificity in information acquisition; Oh et al., 2016;Spiliopoulos et al., 2018;Maule et al., 2000;Gok & Atsan, 2016). Acceleration, evidenced by speeded information processing, is demonstrated here with decreased response times, and Filtration, selective information processing, is also apparent through enhanced selective attention to Flanker arrows (seen in reduced inhibition). ...
... According to an account from Payne et al. (1988), decision-makers adjust their strategies to optimize effort and accuracy in response to time pressure (Dambacher & Hübner, 2015;Gok & Atsan, 2016;Payne et al., 1988). These strategic changes include acceleration (faster processing; Spiliopoulos et al., 2018;Gok & Atsan, 2016) and filtration (increased specificity in information acquisition; Oh et al., 2016;Spiliopoulos et al., 2018;Maule et al., 2000;Gok & Atsan, 2016). Acceleration, evidenced by speeded information processing, is demonstrated here with decreased response times, and Filtration, selective information processing, is also apparent through enhanced selective attention to Flanker arrows (seen in reduced inhibition). ...
... From an econometric point of view, C-Lasso avoids a series of drawbacks of mixture models. In particular, the likelihood function of a finite mixture model usually shows irregularities such as multimodality (Lehmann and Casella 2006;Spiliopoulos et al. 2018), and therefore introduces a complication in detecting the local maximum point that corresponds to the efficient root. 1 On the contrary, the application of a penalized likelihood maximization of C-Lasso produces a unique solution. One other application of this method is provided by Bordt and Farbmacher (2019) who use experimental data of repeated public good games to replicate the classification of behavioral types proposed by Fischbacher et al. (2001) using the C-Lasso mechanism. ...
We apply the classifier-Lasso (Su et al. 2016) to detect the presence of latent types in two data sets of previous contest experiments, one that keeps the grouping of contestants fixed over the experiment and one that randomly regroups contestants after each round. Our results suggest that there exist three distinct types of players in both contest regimes. The majority of contestants in fixed groups behaves reciprocal to opponents’ previous choices. A higher share of reciprocators per group is associated to lower average overspending which hints at cooperative attempts. For experiments in which contestants are regrouped, we find a significantly lower share of ‘reciprocators’ and no significant association between the share of reciprocators and average efforts.
... We did not train the participants long enough to check their proficiency in this case. Time constraints add cognitive load while completing a task and is used in many tasks in games [33]. In our trials before the actual study with our colleagues, they could complete the medium-and short-distance tasks before the allocated times. ...
... Our analysis compared 10 decision policies (playing against each other): the Nash equilibrium, a baseline random policy, and a set of eight heuristics that have been identified as commonly used by actual players in real-life situations (Costa-Gomes, Crawford, & Broseta, 2001;Costa-Gomes & Weizsäcker, 2008;Devetag, Di Guida, & Polonio, 2016;Polonio, Di Guida, & Coricelli, 2015;Spiliopoulos, Ortmann, & Zhang, 2018;Stahl & Wilson, 1994Sutter, Czermak, & Feri, 2013). The decision policies are defined in Table 2; more detailed examples of how to compute them can be found in Appendix B. ...
... ⍀ refers to the rank order of the decision policies according to their complexity (as analyzed in Spiliopoulos, Ortmann, & Zhang, 2018), with 1st referring to the most complex one and 6th to the least complex one. This document is copyrighted by the American Psychological Association or one of its allied publishers. ...
... First, as discussed earlier, the calculation of a mixed strategy NE is computationally demanding. 12 Therefore, we consider only the subset of NE that could plausibly also be implemented as a descriptive model of behavior in one-shot games, as there is some evidence of purestrategy NE use in simple games (Rey-Biel, 2009;Spiliopoulos et al., 2018;Stahl & Wilson, 1995). Second, the epistemic interpretation of a mixed strategy, particularly in a one-shot game, is controversial: 13 "We are reluctant to believe that our decisions are made at random. ...
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) 2019 APA, all rights reserved).
... One way to potentially lower average strategic levels is to force subjects to make decisions more quickly, under time pressure. For example, Spiliopoulos, Ortmann, and Zhang (2018) found that people tend to choose more heuristic strategies when facing a time constraint, in several normal form games. If higher levels of strategic thinking take more time (e.g., Brocas et al. 2014), time pressure will correspond to reducing the value of τ. ...
Coordination games often have salient “focal points”. In games where choices are locations in images, we test for the effect of salience, predicted a priori using a neuroscience-based algorithm, Concentration of salience is correlated with the rate of matching when players are trying to match (r=.64). In hider-seeker games, all players choose salient locations more often, creating a “seeker’s advantage” (seekers win 9% of games). Salience-choice relations are explained by a salience-enhanced cognitive hierarchy model. The novel prediction that time pressure will increases seeker’s advantage, by biasing choices toward salience, is confirmed. Other links to salience in economics are suggested.
