Commentary/BenÛey et al.: Mapping collective behavior and social cognition (medial prefrontal cortex [MPFC], posterior cingulated cortex [PCC]), and greater memory encoding (hippocampus) was associated with more positive post-scan sentiments. More positive descriptions were associated exclusively with neural activity in the temporal parietal junction (TPJ), a region often Hnked to perspective taking (Saxe & Kanwisher 2003). The implication of tliis latter finding for understanding die first stages of idea propagation (contagion or "virality") is that individuals may be socially motivated right from the moment they encounter a new idea or potential "même." In other words, as we have noted here, even in the absence of others (tlie "independent" end of a classification scheme), we may rely heavily on assumptions of what others wül tliink, feel, and believe as we take in new information and prepare to make it useful to others. The use of automated linguistic analysis to connect brain to behavior allows scaling from the first order (those exposed to the original idea), to the second (those exposed to word-of-mouth description from first-order individuals; Falk et al. 2013), and so on, providing novel insight regarding the underl)'ing mechanisms involved in the spread of ideas (Berger & Milkman 2012). In sum, although we question tlie east-west dichotomy of the dimensions proposed by Bentley et al. we fuUy agree with the underlying premise that tools from a range of social science disciplines are needed to more deeply ground our understanding of big data. We have presented initial examples of how social psychological and neural findings might add different perspectives to the framework proposed, and how linguistic tools can link levels of analysis. Additional research widiin these fields wül further expand our ability to contextuaHze big data in the new media landscape and beyond.
Independent decisions are fictional from a psychological perspective doi:10.1017/S014052.5X13001830 Hans-Rüdiger Pfister^ and Gisela Böhm'' ^institute ot Expérimentai industriai Psychoiogy, Leuphana University Lüneburg, 21335 Lüneburg, Germany; "Facuity ot Psychoiogy, University of Bergen, 5015 Bergen, Norway. [email protected] [email protected]
Abstract: Contrasting independent with socially influenced decision making does not capture crucial differences in decision making. Independence is fictional, and social influences substantially permeate preference constmction. A distinction between deliberate and intuitive decision maldng would be more useful, and the problem iii die big-data era is deciding when it is better to rely on deliberation and when to trust one's intuitions.
Bentley et al. propose a two-dimensional map that aims to describe decision making in the big-data era. We believe that by analyzing how the big-data era - together with the omnipresence of the Intemet and the increasing interconnectedness of people via social networks - may shape decision making, Bentley et al. have chosen a highly relevant and timely topic. However, we think that the east-west axis, which contrasts independent and socially influenced decision making, is not a suitable dimension to use in this context because it is not well grounded in the field of psychological decision research. We identify two shortcomings: First, the type of behavior that they are trying to classify is ambiguously defined. Second, the notion of independent decision making is not empirically tenable. First, much of what Bentley et al. refer to as "decision making" is not decision making proper as the term is used in the peitinent literature on psychological decision-making research (Baron 2007;
Hardman 2009; Kalmeman & Tversky 2000; Koehler & Han'ey 2004) or behavioral economics (Camerer et al. 2004; Kalmeman 2003; Loewenstein 2007). Most importantly, Bendey et al. confound decision making and leaming, which is, surprisingly, a fact that they acknowledge tliemselves: "we blur the distinction between leaming and decision making" (target article, sect. 2, para. 7). Psychologically, however, decision making and leaming involve fundamentally different processes. A decision-making process anticipates the future, whereas leaming generates our memories. The acts of anticipating the future (Cilbert & Wüson 2007) and remembering the past (Loftus & Picki-ell 1995) are prone to systematic biases. Furthermore, human decision making is notoriously resistant to leaming from experience (Brehmer 1980; Kahneman 2011), leading to a multitude of non-optimal choices. Many of Bentley et al.'s signature pattems are pattems of optimal behavior, such as microeconomic utility maximization or the ideal free distribution. Such pattems may emerge under veiy specific conditions (environmental regularify, constant feedback) but are usually not present in situations of individual choice. In fact, we think that a more appropriate mapping would include leaming and decision making as oithogonal dimensions, with the leaming axis contrasting basic opérant conditioning at one end with socially mediated leaming at the other end. Second, the contrasting notions of independent versus .socially influenced decisions seem iU-founded from a psychological perspective. Assuming an independent decision maker in Bentley et al.'s sense invokes the traditional model of a selfish utilify-maximizing hoino oeconomicus who is equipped witli a set of fixed and immutable preferences. This model has been rendered untenable by accumulating empirical evidence (Akerlof & Shiller 2009; Ariely 2008; Cüovich et al. 2002; Kahneman 2003, 2011; Loewenstein 2007). Human preferences, which are the primitive elements of decision making, are anytliing but fixed and stable. A major finding from behavioral decision research is that preferences are constructed rather than given and are highly dependent on die context. Studies on constmctive processes (Lichtenstein & Slovic 2006), as well as studies on preference reversals and framing effects (Kalmeman & Tversky 2000; Tversky & Simonson 1993; Tversky & Thaler 1990) have demonstrated that preferences are subject to a large variefy of contextual factors. An example is the ubiquitous role of anchors and reference points in determining utilities (Kalmeman & Tversky 1979; Ordonez et al. 2000; Thaler & Sunstein 2008); for example, by simply changing the reference point (tlie default) from opt-in to opt-out, tlie preference conceming organ donation can be changed by 40% in favor of donation (Johnson & Goldstein 2003). In sum, preferences are always shaped by a multitude of contextual influences, many of wlúch are social in origin. If die westem end of Bentley et al.'s east-west dimension cannot be adequately characterized as independent decision making, then we must ask what defines its opposite. Bentley et al. label tlie eastem pole as socially influenced decision making where people thoughtlessly copy what others do. But is Ulis tlie opposite of independent decision making? We view this as just a special, though common, case of a constmcted preference: In many situations, the modal behavior of odiers may serve as a natural reference point; and relying on what others do is just an instance of a fast and fmgal heuristic (Cigerenzer et al. 2002). We believe that it is not so much social influence that characterizes the decision-making processes at the eastem pole-because social influence is also present at die westem pole - but their more intuitive character. What then could serve as a psychologically founded decisionmaking dimension? We suggest that any realistic model of humtm decision making should be based on the roles of affect and emotions (Damasio 1994; Loewenstein & Lemer 2003; Pflster & Böhm 2008; Zeelenberg & Pieters 2006). Emotions shape human preferences, and they provide the cmcial Unk between deciding and acting (Böhm & Pfister 2000; Pfister & Böhm 2008; Zeelenberg & Pieters 2006). Furthermore, a look BEHAVIORAL AND BRAIN SCIENCES (2014) 37:1
Commentary/Benthy et al.: Mapping collective bebavior at the functional roles of emotions suggests that individual as well as social decisions can be mapped onto specific emotions (Pflster & Böhm 2008; 2012). Some emotions are simple immediate affective reactions, such as joy or disgust, whereas others are cognitively saturated, such as guüt or envy. We suggest that decision making be represented by a dimension that runs from deliberate/emotionally complex to intuitive/ emotionally simple, a distinction emphasized in current dualsystem approaches (Evans 2008; Kalineman 2011). A decision maker may rely either on an effortful process of deliberation and reasoning or on intuitions. Both modes of decision making can be advantageous or misleading, depending on the circumstances (Cigerenzer 2007; Hogarth 2010). We speculate that in a big-data era, it will become a critical issue for decision makers to select the appropriate mode, as the two modes often conflict. An Amazon purchase recommendation may superflcially confonn to a person's intuitive preferences, but may be rejected after some deliberate reasoning. On the other hand, when facing an unmanageable number of options, a deliberate decision might not be feasible, as emphasized by Bentley et al., tlius raising the issue of how to educate our intuitions to survive in times of Hmidess choices.
What shapes social decision making? doi:10.1017/S0140525X13001842 Simon M. Reader^ and ioannis Leris" ^Department of Biology, McGiil University, Montréal, Québec, H3A 1B1, Canada; "Behavioural Biology, Department of Biology, and Helmhoitz institute, Utrecht University, 3584 CH Utreoht, The Netherlands. [email protected] [email protected]
Abstract: Outcome transparency and the weight given to social information both play important roles in decision making, but we argue that an overarching influence is the degree to which individuals can and do gather information. Evolution, experience, and development may shape individual specializations in social decision making tliat carry over across contexts, and these individual differences may influence collective behavior and cultural evolution.
Bentley et al. discuss how technological advances, particularly online connectixity, change human decision making and collective behavior and their empirical study. They nsefully caution that although large-scale online data provide new opportunities for the understanding of hnman behavior, there are also new pitfalls. Bentley et al. present a deliberately simplified conceptual map of different types of hnman decision making, with two dimensions: the degree to which personal versus social influences shape a decision, and the transparency of the payoffs of a decision. We agree that these are important determinants of decision making, and the two dimensions provide a useful simplification for several applications. However, we would like to draw attention to other determinants of decision making, particularly the influence of past experience, individual differences, and payoff structure, all of which may affect large-scale patterns. The weighting of personal experience ("individual information") against infonnation provided from others ("social information") is a key determinant of human decision making, and numerous factors can determine this weighting, such as tlie predictability of the environment, the relative costs of social and individual information, or die availability of suitable models to lcam from (Boyd & Richerson 1985; Laland 2004). Erequently, individual and social infonnation wül together determine a decision (Kendal et al. 2009; Salganik et al. 2006). However, adaptive decisions can be made without gathering information (Dalí & Johnstone 2002; Stephens 1991), and thus the intensity of information use in a decision may predominate over whether individual
BEHAVIORAL AND BRAIN SCIENCES (2014) 37:1
versus social information is utilized. Important decisions in particular are likely to involve substantial use of botli individual and social infonnation. Based on work on animal personalities (e.g., Marchetti & Drent 2000), we suggest that tlie amount of information gathered for decision making may consistently differ between individuals, with certain individuals more likely to utilize both individual and social information. That is, we predict individual and group differences in their sensitivity to infonnation, in the readiness to search for information, as well as in the strategy used to obtain infonnation. Bentley et al. discuss several aspects of the transparency of decision payoffs, such as whether a right or wrong decision is immediately detectable, whether a payoff can be assessed before a decision is made, the equivalence of payoffs, tlie degree of understanding of the processes that determine payoffs, and the ease of assessing who to leam from. Since these aspects of transparency may not covary, we urge caution in treating payoff transparency as a unitary entify. This is particularly important because payoff transparency, together with the costs of decision making, are likely to determine whether social or individual information is utilized: When the best decision can easily be determined individually, social information wifl be less advantageous (Boyd & Richerson 1985; Kendal et ÍÚ. 2009). Thus, the two dimensions of Bentley et al.'s conceptual map are not independent. Payoff transparency may depend on numerous additional characteristics, such as the way that competition or frequency dependence affect payoffs (McNamara & Eawcett 2012), the shape of the "adaptive landscape," and thus the degree to which similar past decisions inform current decisions, and the kind of social information that models provide (Beppu & Griffiths 2009; Rendell et al. 2011). Assessment of payoff transparency is further complicated since a single decision may have multiple, possibly conflicting, payoffs in different domains. Eor example, depending on circumstances, deviating from group behavior may have economic benefits but social costs, or, alteniatively, independent innovation may be sociafly rewarded but economicafly costly (Day et al. 2001; Grève 2003). Bentley et al. argue that differences in agent competencies will not be visible at an aggregated scale. However, groups may differ in the way payoffs are assessed, potentially resulting in different processes in different groups. Eor example, cMdren and adolescents differ in the way they assess losses and long-tenn rewards, compared to adults (Aïte et al. 2012). Simüarly, experts may outperform the "wisdom-of-the-crowd" depending on the task (Krause et al. 2010), meaning that individual competencies may have significant influence. A stiiking example of expertise trumping the wisdomof-the-crowd is the victory of top-ranked chess player Magnus Carlsen over a large online audience (referred to as "The World") that participated by choosing one of three avaüable moves proposed by a group of chess grandmasters (McClain 2010). We consider whether humans can switch freely between incorporating different kinds of information in tlieir decisions. Each decision could be optimized by determining which informationgathering strategy to apply, which may itself rely on sociiü cues (Toelch ct al. 2011). However, the costs of assessing which strategy to empk)y may outweigh any achieved benefits, and prior knowledge and individual characteristics, perhaps in combination witli evolved predispositions, could shape the strategy employed. Current, past, or early life experience can be used to determine which information-gathering strategy is most likely to be profitable. Eor instance, humans playing a computer game apparendy used the degree of environmental variability as a heuristic cue for determining whether to copy others (Toelch et al. 2009). In nonhuman animals, recent and early Ufe-expcrience detennines the reliance on social information (Chapman et al. 2008; Dawson et al. 2013; Katsnelson et al. 2008; Lindeyer et al. 2013). Thus, experience and development may influence future decision making, resulting in carryover effects where experience with one problem type or in one domain influences the decision
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