Commentary/Bentiey et al: Mapping collective behavior fully connected ^ networks

networks with strong group structure Figure 1 (Fortunato et al.). A proposed network dimension, relevant to the northeast and southeast quadrants. At one extreme are networks with veiy strong social group stmcture, limiting access to choices for social learning, and at the other extreme are networks where everyone is connected to everyone else. As an example, consider tlie part of this augmented map with strong social group structure and purely social leaming ("I'll have what she is having") where each individual imitates a r;mdom network neighbor. This is the same mechanism as in die voter model of statistical physics (Gastellano et al. 2009). In this example, depending on the details of the decision-making process, the collective outcome may or may not depend on network stmcture. If the individuals update their choices perpetually as above, the model leads to a consensus where, regardless of network stmcture, a single choice prevails. However, if the individuals choose only once and stick to their choice, die distribution of die final popularity of choices heavily depends on network stmcture (Fortunato & Gastellano 2007). Even with repeated choice updating, if an undecided state is added to the model, strong social group stmcture gives rise to long-lasting metastable states with each group sticking to its own choice (Toivonen et al. 2009). If each individual were to always pick the majority choice in its network neighborhood, each group would converge to its own choice, largely unaffected by those of other groups. Gonsider now the other extreme of the network dimension, that is, a fuUy comiected network where each individual sees the choices of aU others. Here, blind imitation by copying the choice of a randomly chosen individual is equivalent to picking a decision with probability proportional to its popularity in the population. Thus, the mechanisms attributed to soudieast (bUnd imitation) and noitlieast (choosing on the basis of populaiity) ai-e die same. This is preferential attachment, yielding a lognormal popularity distribution if the adoption probabilities are subject to random fiuctuations, as Bendey et al. state. However, in the absence of such fluctuations, die resulting popularity distiibution is a power law (Barabási & Albert 1999). Indeed, die profile of the popularity distribution of Wikipedia pages is consistent widi a power law, not with a lognormal distribution (Ratkiewicz et al. 2010). We would also like to argue diat the distinction between the northeast and southeast quadrants (transparent vs. opaque payoffs) may at times be difficult as die payoffs may be socially generated - unrelated to intiinsic qualities of the options, but instead a product of how social influence is mediated through the network. Watts (2011) points out how the success of hits in different sectors of human activity, such as art (Mona Lisa), literature (the Harry Potter series), and technology (the iPod) caught experts by suqjrise. For example, eight publishers rejected the first Harnj Potter manuscript. Experiments by Salganik and colleagues (Salganik et al. 2006) showed that the same set of songs were ranked differently by comparable groups of subjects depending on the extent to which they were exposed to the decisions of odiers. The difference between a smash hit and failure may dien be due to social cascades arising from initial random fluctuations, giving the incipient winner a decisive early advantage over its competitors (Fig. 1).

i\/lissing emotions: The Z-axis of collective behavior doi:10.1017/S0140525X13001738 Alejandro N. Garcia, José M. Torralba, and Ana Marta González Institute tor Culture and Society, Editicio de Bibliotecas, Universidad de Navarra, 31080 Pamplona, Spain. [email protected] [email protected] [email protected] http://www.unav.es/centro/cemid

Abstract: Bentley et al. bypass the relevance of emotions in decisionmaldng, resulting in a possible over-simplification of hehavioral types. We propose integrating emotions, both in the north-south axis (in relation to cognition) as well as in the west-east axis (in relation to social infiuence), by suggesting a Z-axis, in charge of registering emotional depth and involvement.

Emotions influence both individual and collective behavior. Yet, in their account of collective behavior, Bendey et al. do not mention emotions even once, and it is not clear how they could be integrated into their proposal such that it would truly account for the discrete decisions that individuals face. The two axes diey propose for organizing the analysis of big data are meant to measure the degree of social influence and transparency of payoffs in discrete choices, yet the way individual choices are influenced by emotions cannot simply be assimilated by any of the given variables. It has become increasingly clear diat economic decisions cannot be explained widiout taking the emotions into account (Berezin 2005; 2009). Wliile Bentley et al. recognize diat "the map requires a few simplifying assumptions to prevent it from morpliing into something so large that it loses its usefulness" (target article, sect. 2, para. 7), we argue diat neglecting emotions can only result in a distorted and impoverished account of behavioral types, which reduces, if not spoils, the usefulness of the map altogether. We need "more sensitive mediodologies with winch to capture these complex multidimensional decision-making processes" (Williams 2000, p. 58). Perhaps Bentley et al. have ignored emotions on the basis of the assumption that they fall into the category of "opaque and socially influenced behavior." But this assumption would be wrong, for emotions do not only influence cognition and decision maldng; they are also present, in varying degrees, in seemingly independent behavior. By not explicitly including the role of emotions in decision making, Bentley et al. are likely to have arrived at conclusions based on spurious variables. The way emotions influence cognition cannot be adequately represented in terms of transparency versus opacity of the payoffs and risks along the north-soudi axis. While there are cases of "collective effervescence" (Durkheim 2001, p. 171) in which BEHAVIORAL AND BRAIN SCIENCES (2014) 37:1

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Commentary/BenÛey et al.: Mapping collective behavior emotions influence cognition to tlie point that they obscure any thought of the consequences of a given action, at other times emotion does not obscure tlie recognition of consequences and yet agents still deeide to act regardless of the consequences. Think, for example, of someone who sacriflces something for a friend: While it might be said that in this case the agent weighs two options and finds one of them more rewarding than the other, there is room to argue that emotions cannot be accounted for in terms of a cost-benefit analysis (Archer & Tritter 2000). To give a more plausible account of behavioral types that indirectly reflects personal and cultural values (Hechtman et al. 2012), emotions should be considered as an independent variable. Thus, although a particular agent might have clear knowledge of tlie objective costs and benefits involved in a pariicular choice, her decision may not be determined by those considerations. Indeed, it is completely plausible that the emotional variable sometimes trumps other variables in the decision-making process. Yet, in addition to their role as independent variables, emotions can influence the cognitive process itself tliat is, cognitive evaluation is not independent of emotional dimensions. The information we consider relevant in a given context depends on emotional states. As Bandelj (2009) notes: [E]motions serve as one of the chief meehanisms to constrmn and direct our attention, and hence frame our decisions. Emotions define what shall be considered as relevant for any paiticular action prohleni. In addition, during the process of selecting optimal means for desired goals, emotions help us narrow down die range of plausible alternatives and help us rank these alternatives, (p. 352) The level of emotional involvement regulates cognitive processes as well as what counts as a cost or a beneflt in a pariicular situation. Accordingly, transparency and opacity do not depend only on the

Figure 1 (Garcia et al.).

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objective information provided, but also on the degree of emotional involvement. Cognitive transparency is not equivalent to "emotional detachment"; certainly, very often it is only through emotions and the evaluations they entail, that is, through some degree of emotional involvement, that we come to realize the seriousness of certain injustices. Presence of emotions along the west-east axis cannot be reduced to the "opaque and socially influenced behavior." It is certainly t m e tliat emotions are present in socially influenced behavior - if only to avoid cases of "cognitive dissonance" (Festinger 1964, p. 5). From this perspective, we could even inquire into the extent to which the Intemet influenees emotional reactions to events and, hence, the very nature of big data collected through it. However, reducing the presence of emotions to the quadrant of "opaque and socially influenced behavior" would be misleading. This is so for two reasons: first, because emotions can also be the motive for isolationist behavior, wliich at first sight could resemble independent behavior, and second, because liighly independent and calculated decisions are sometimes made precisely to create some sort of emotional bond, and it is precisely the Intemet, with its extensive social media, wlúch often serves this purpose (iUouz 2007). In light of these considerations, we think that Bentley et al.'s empirical framework for big-data research would benefit from introducing a Z-axis that registers tlie intensity of emotions influencing individual choices at any given moment. While this inclusion entails complicating the behavioral types (see graph in our Fig. 1), the result not only provides a more plausible account of human behavior, but arguably better serves die practical ends that the authors advance at the end of the article. After all, as marketing researchers know well (Bagozzi et al. 1999), when decisions are mostly based on emotions, providing too much information may be eounterproductive; the imporiant

Graph 1: A tentative reformulation of behavioral types that includes emotions.

BEHAVIORAL AND BRAIN SCIENCES (2014) 37:1

Commentary/Bentley et al: Mapping collective behavior thing, then, is not to provide too much infomiation, but rather to provide the infonnation relevant to the agent (see Fig. 1).

Capturing the essence of decision making should not be oversimplified doi:10.1017/S0140525X1300174X Ewa Joanna Godziñska and Andrzej Wróbel Department of Neurophysiology, Nenci

Missing emotions: the Z-axis of collective behavior.

Bentley et al. bypass the relevance of emotions in decision-making, resulting in a possible over-simplification of behavioral types. We propose integr...
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