Jesse Fenneman, Willem E. Frankenhuis. Evolution and Human Behavior, Volume 41, Issue 4, July 2020, Pages 261-273. https://doi.org/10.1016/j.evolhumbehav.2020.02.005
Abstract: Evolutionary social scientists have argued that impulsive behavior is adaptive in harsh and unpredictable conditions. Is this true? This paper presents a mathematical model that computes the optimal level of impulsivity in environments varying in harshness and unpredictability. We focus on information impulsivity, i.e., choosing to act without gathering or considering information about the consequences of one's actions. We explore two notions of harshness: the mean level of resources (e.g., food) and the mean level of extrinsic events (e.g., being the victim of a random attack). We explore three notions of unpredictability: variation in resources, variation in extrinsic events, and the interruption risk (the chance that a resource becomes unavailable). We also explore interactions between harshness and unpredictability. Our general model suggests four broad conclusions. First, impulsive behavior is not always adaptive in harsh and unpredictable conditions; rather, this depends on the exact definitions of harshness, unpredictability, and impulsivity. Second, impulsive behavior may be adaptive in environments in which the quality of resources is low or high, but is less likely to be adaptive when their quality is moderate. Third, impulsive behavior may be adaptive when resource encounters are likely to be interrupted. Fourth, extrinsic events have only a limited effect on whether impulsive behavior is adaptive. We discuss the implications of these findings for future research, consider limitations, and suggest future directions.
Keywords: ImpulsivityHarshnessUnpredictabilityBayesian inferenceLife history theoryFormal model
4 Discussion
Evolutionary social scientists have argued that impulsive behavior is adaptive in harsh and unpredictable environments. We have developed a formal model that explores how commonly used definitions of harshness and unpredictability affect the optimal level of information impulsivity. Our results show that this hypothesis is not universally true, but rather, depends on the exact definition of harshness, unpredictability, and impulsivity; harsh and unpredictable environments can favor high or low levels of impulsivity, or have no effect on impulsive behavior.
Our model suggests five conclusions about how harshness and unpredictability shape the optimal level of impulsive behavior. Two of these are also supported by existing models: individuals should sample more cues when the prior uncertainty of resources is higher (i.e., when the variance in resource quality is high); and individuals that are close to a somatic threshold (starvation or satiation) should sample more information, regardless of the state of their environment. Three other findings may be novel. First, impulsive behavior is adaptive when the resource quality is either low or high, but not when it is moderate. Second, impulsive behavior is almost always adaptive when resources are likely to be interrupted. Models of temporal impulsivity often find that temporal impulsivity increases as interruptions become more common. However, to our knowledge, this is the first model that finds similar effects on information impulsivity. Third, the mean and variance of extrinsic events only affect impulsivity when agents are in a very bad or a very good state. This is surprising because harshness is commonly defined (although not typically measured, see section 1.2.1) as a high rate in which external factors cause disability and death.
The conclusion that harshness and unpredictability can have multiple influences on impulsivity highlights the need for clear and explicit definitions. Although different interpretations of harshness and unpredictability are typically empirically related (e.g., resource scarcity can increase violence and disease), they are conceptually different. An environment can simultaneously be harsh and unpredictable in some sense, but affluent and predictable in others. Empirical support for the adaptive impulsivity hypothesis is mixed (see section 1.1). This might be partly due to the jingle fallacy, the erroneous belief that two constructs are the same because they have the same name. However, if empirical results depend on what notion of harshness, unpredictability, or impulsivity is measured, findings from one study might not generalize to other studies or to other populations. This makes it difficult for studies to incrementally build upon each other, stifling academic progress. We therefore strongly recommend that future studies use explicit, ideally formal, definitions of harshness and unpredictability. Such explicit definitions can help improve empirical measurements of harshness and unpredictability. For instance, future measurements of harshness could explicitly differentiate between resource scarcity and high levels of extrinsic morbidity-mortality.
4.1. Formalizing life history theory in the social sciences
Our model also contributes to a larger conversation about how to use life history theory in evolutionary social sciences. A recent bibliometric analysis shows that in the previous decade the life history literature has fragmented into different clusters with dividing lines between the evolutionary psychology, evolutionary anthropology, and non-human animal literatures (Nettle & Frankenhuis, 2019). Alarmingly, studies within the evolutionary social science cluster have few ties with formal models of life history theory. These weak connections are problematic, because references are sometimes used in support of claims that are different, absent, or even contradictory to the source model. One example is the proposed fast-slow continuum. Although its existence is often described as a fundamental prediction of life history theory (Ellis et al., 2009), formal support for the fast-slow continuum is limited and mixed (Mathot & Frankenhuis, 2018; Zietsch & Sidari, 2019). Some models show that harsh and unpredictable conditions can favor slow life histories (e.g., Abrams, 1993, Baldini, 2015). Similarly, our model shows that one kind of impulsivity, which is often viewed as part of a fast life history, is not necessarily favored in harsh and unpredictable environments.
4.2. Empirical predictions, limitations, and future directions
All models are simplifications of reality (Smaldino, 2017). However, they differ in whether they are general or specific (Houston & McNamara, 2005; Parker & Smith, 1990). The goal of a general model is to study abstract qualitative patterns. For instance, a prisoner's dilemma model captures the logic of cooperation and defection between two rational players – it does not matter whether the players are people, companies, or rivaling states. The parameters of general models are often difficult to operationalize, predict, and measure. Specific models study the dynamics of a particular real-world system. The parameters of these models are frequently based on empirical data, and these models might provide predictions. We have presented a general model; our goal was to provide a formalization of the adaptive impulsivity hypothesis. As such, we made simplifying assumptions. These assumptions allowed us to explore a decision problem in depth, facilitating theoretic insight about the ways in which key variables interact with each other. Simple models are well suited to producing such insights, but at a cost to realism (Levins, 1968). We think this is acceptable, because our primary goal is not to make empirical predictions. However, this does not necessarily mean that the conclusions of our model cannot be used as empirical prediction. Rather, this depends on the extent to which the assumptions of our general model capture essential features of real environments. If this match is sufficiently high, the conclusions of our model on how harshness and unpredictability shape impulsive behavior can be used as empirical predictions. Estimating this match is difficult, if not impossible. There are, however, several limitations that reduce realism and limit the scope of our model. These limitations are hierarchical: we can only address some limitations (e.g., our model does not include life history trajectories) after we have addressed more fundamental limitations (e.g., our model does not address development or environmental change). Here we discuss four fundamental limitations. For each limitation we discuss how potential extensions can incorporate more realistic and more complicated assumptions that address these limitations.
First, in order to reduce complexity we assumed that the parameters of the environment are fixed within and between generations. We further assumed that an agent learned the (meta) parameters of its environment through its evolutionary and developmental history. Although extreme outcomes may be unexpected, they do not change an agent's beliefs about its environment. For some organisms a fixed world assumption may be realistic: if the rate of environmental change is slow compared to the lifespan of an organism, the environment might appear to be fixed from that organism's perspective (Fawcett & Frankenhuis, 2015). However, for species with a longer life span, such as humans, the environment might change both temporally (e.g., due to economic cycles) and spatially (e.g., due to labor or educational migration). In a fixed environment an organism ‘only’ has to infer the value of an encountered resource. In a varying environment, it also has to infer the current state of the environment and forecast what the future might hold. This results in a tradeoff between exploration (sampling information) and exploitation (saving costs by relying on current estimates of the environment). Moreover, in a varying environment, there might be lean years where resources are scarce and/or extrinsic events more extreme. An organism can buffer against such variability by storing resources. This might increase (to save costs on information gathering) or decrease (to reduce the variance in outcomes) impulsivity. Besides reducing realism, this assumption also reduced the scope of our model: unpredictability is often interpreted as changes in the environmental state (e.g., this kind of unpredictability is the focus of Ellis et al., 2009). Future models could incorporate both temporal and spatial unpredictability.
Second, our model includes no development. We studied organisms that (a) are fully developed at birth, (b) are affected by the environment regardless of their age, and (c) reproduce only at the end of life. These assumptions do not hold for many species, including humans. Rather, individuals typically go through early developmental stages in which they acquire the skills needed to integrate information. If the individual faced early-life adversity, or if this acquisition is costly or time consuming, investing in this skillset might not outweigh the cost. Moreover, both the young and the old might be more affected by resource scarcity and extrinsic events than adults. In hunter-gatherer societies, only adults produce more food than they consume (Kaplan, Hill, Lancaster, & Hurtado, 2000). Consequentially, in lean years the old and young might be more susceptible for starvation than adults. Similarly, negative extrinsic events such as disease and violence might disproportionally affect the young (who are less able to defend themselves) and the old (who might be weakened due to senescence). Finally, we studied an organism that is semelparous, rather than iteroparous. However, in many species fecundity and fertility often peak during middle age. As both reproduction and the subsequent investment in offspring are costly, individuals in middle age might face a higher demand for resources. Future models can build in age structure and reproduction, with survival and fecundity differing at different ages, and explore how such selection regimes shape the optimal level of impulsivity.
Our model can also be extended to include developmental processes in order to explore to two empirical patterns. First, impulsivity and risk taking are highest during adolescence, when individuals enter the mating competition market (Figner, Mackinlay, Wilkening, & Weber, 2009; Steinberg, 2007). For risk behavior, a common explanation is that securing a high quality mate requires intense competition for resources and social status (Ellis et al., 2012), which demands high levels of risk taking (e.g., engaging in physical fights). Future models could examine whether this increased need for resources and social status likewise results in more impulsive behavior. Another empirical pattern is the paradoxical (but robust) finding that both behavioral tasks and self-report questionnaires predict real-world impulsivity, yet the two sets of measurement show little to no correlation (Cyders & Coskunpinar, 2011; Reynolds, Ortengren, Richards, & de Wit, 2006; Stahl et al., 2014). A popular explanation is that both sets of measurements tap into separate constructs. Self-reports measure a stable baseline of impulsivity (i.e., trait impulsivity), whereas behavioral tasks measure the capability to flexibly deviate from this baseline in situations that require higher or lower levels. This explanation raises such interesting questions as: Why there is a baseline? Why do we not always adjust our impulsivity to match the current situation? Why do individuals differ in their baseline levels? Is this baseline continuously updated throughout development, or are there sensitive periods in which the baseline is set for the rest of life? Part of the answer to these questions might be that flexibility comes at a cost. For instance, the cognitive machinery needed to make constant adjustments might be expensive to maintain. If we always need the same level of impulsivity – for instance, when our environment is sufficiently stable – the cost of plasticity might outweigh its benefits (Fawcett & Frankenhuis, 2015). Moreover, if the environment is very stable, the best strategy might be to set a fixed baseline early in life (i.e., a sensitive period). Future models can explore these questions by incorporating developmental processes.
Third, we committed to the ‘behavioral gambit’: we studied a single behavioral trait in isolation, and implicitly assumed that the expression of this trait is not hindered by other life history, behavioral, or physiological traits (Fawcett, Hamblin, & Giraldeau, 2013). Furthermore, our model did not address genetic, developmental, physiological, or cognitive limitations that prevent an organism from following the optimal policy. In real life there are limitations. For example, we assumed that organisms behave as-if they perform Bayesian updating. However, Bayesian updating is computationally expensive at the best of times, and computationally intractable in most realistic situations (Trimmer, McNamara, Houston, & Marshall, 2012; van Rooij, Wright, Kwisthout, & Wareham, 2018).
The behavioral gambit is a useful simplification when testing under what environmental conditions impulsivity might be adaptive. However, it limits the scope and realism of our results. Future extensions might explore two different avenues. First, they can incorporate more realistic cognitive processes. For example, future models can study agents that rely on heuristics that human decision-makers are known to use. This extension can study in which environment a specific heuristic performs well, and when it performs poorly. Alternatively, rather than simulating agents that use known heuristics, future models can use the computed optimal policies to explore new heuristics. Specifically, based on modeling results, future research can explore which heuristics would allow animals to approximate optimal decisions. Second, they can increase realism by incorporating other behavioral traits. Such a model can provide novel insights for two different debates. Different notions of impulsivity are only weakly correlated or even uncorrelated (section 1.2.3). A model incorporating multiple types of impulsivity can explore whether environmental conditions moderate the correlation between different conceptualizations. That is, it can explore whether some environments favor high (or low) levels of all types, whereas others favor high levels of one type but low levels of the other. Alternatively, extensions can incorporate other behavioral, physiological, or life history traits that are proposed to cluster on a fast-slow continuum. This extension can test the claim that harsh and unpredictable environments result in faster life-history strategies.
Fourth, we assumed that agents did not interact, nor needed to consider the behavior of other agents (i.e., our model is not game theoretic). This assumption is reasonable for some decisions. For instance, if resources are (practically) infinite, the actions of one agent do not noticeably change the number of available resources (e.g., when job supply is high, accepting a job does not meaningfully decrease the total number of available jobs). In other decisions agents do interact, but only indirectly. In this case, accepting may reduce the resources available for other agents. However, the behavior of other agents does not influence the consequences of an action during a resource encounter. For instance, two predators may share overlapping domains. Although they rarely are in close proximity, resources consumed by one are no longer available for the other. However, whether or not one predator should give chase to prey does not depend on the actions of the other predator. Our model can incorporate some indirect interactions by changing the parameters of an environment. For instance, our graphical interface allows users to increase or decrease the interruption rate (e.g., prey might be more or less easily scared) or to assume that resources are non-normally distributed (e.g., competitors might be more likely to consume positive than negative resources). However, in many real-world decisions an agent does need to consider the actions of other agents. For instance, resources might become scarce if everyone acts impulsively. If so, acting impulsively may be the only way to collect resources. Such policy, where one is impulsive because everybody else is, results in a positive feedback loop that might increase impulsivity. Alternatively, high levels competition may foster selective cooperation, which requires low levels of impulsivity. It can also result in even more complex patterns, where multiple phenotypes coexist, or the population might cycle between multiple phenotypes (Bear & Rand, 2016; Tomlin, Rand, Ludvig, & Cohen, 2015). It will be hard if not impossible to predict outcomes without building the model. Future models might therefore incorporate interactions between agents.
To end, we have presented a formal model of the increasingly common claim impulsive behavior is adaptive in harsh and unpredictable environments. Our results show that this hypothesis is not universally true, but rather, depends on the exact definition of harshness, unpredictability, and impulsivity. We hope our model will contribute to the corpus of formal models of theories that feature centrally in the evolutionary social sciences.
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