دانلود رایگان مقاله لاتین هنجار متعادل کننده از سایت الزویر
عنوان فارسی مقاله:
شواهد و هنجارهای متعادل کننده در تکامل فرهنگی
عنوان انگلیسی مقاله:
Balancing evidence and norms in cultural evolution
سال انتشار : 2015
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بخشی از مقاله انگلیسی:
Norms and the burden of social proof
The bBOP model (MacCoun, 2012) describes the probability that an individual will switch positions on a dichotomous issue as a function of ‘‘strength in numbers’’ favoring the opposite position in a local population. The acronym ‘‘bBOP’’ stands for ‘‘bidirectional burden of proof’’ – one of a family of similar models in MacCoun (2012). The notation for this and other models discussed in this paper appears in Table 1 and the equation specifying bBOP appears in Table 2. MacCoun (2012) shows how the model can be used as a common frame of reference for behavior in studies of conformity, group deliberation, diffusions of innovation, and neighborhood change. Consider a situation where an actor has reached an opinion on some dichotomous issue or choice, adopting a position or behavior or choice we will call Option 0. The actor then encounters a collection of other people, some of whom have made the opposite choice, Option 1. According to bBOP, the probability that the actor will now change from Option 0 to Option 1 is given by a logistic threshold function that compares the proportion (s) of ‘‘sources’’ (S) who favor the position opposite one’s own in a population of size N (i.e., s = S/N) to a threshold parameter (b) that can be interpreted as the actor’s perceived ‘‘burden of social proof’’ – the point at which Option 1’s popularity is sufficiently high to begin tipping her toward switching from Option 0 to Option 1. Fig. 1 shows how the probability of influence varies with the location of the threshold and the popularity of the opposing position. When b is near 1 the actor places a steep burden of proof on the other side and is thus quite resistant to change. When b is at .5, the burden is shared by both sides, producing an implicit ‘‘majority wins’’ rule, even in the absence of any formal group procedures for consensus. When b is near 0, the actor is almost completely susceptible to any social influence to change positions. The c parameter represents the ‘‘clarity’’ of the matching-tothreshold process. Clarity is inversely related to variance at both the individual and aggregate levels. At the individual level, clarity reflects how strictly one enforces the b threshold, and thus low c can reflect uncertainty or fuzziness about whether the level of social consensus exceeds one’s personal threshold. At the collective level, c is inversely related to the standard deviation of the distribution of b across actors, so that a high clarity level implies a high degree of consensus about the threshold – a shared sense of where the burden of social proof lies in this situation. When c is very high, the model produces a hard threshold and predicts a step function; when c is very low, the model produces a soft threshold and predicts that choice becomes increasingly random. Fig. 2a and b shows the effect of clarity under two different threshold levels. When b = .5 (Fig. 2a), as clarity increases the function begins to resemble a formal ‘‘majority wins’’ voting rule. But when b is near 0 (Fig. 2b), only one or two endorsers may be sufficient to persuade everyone to adopt their position, and as clarity increases the function suggests an implicit ‘‘Truth Wins’’ norm indicating that the group has some shared conceptual scheme (be it arithmetic, logic, theology, or economic theory) for recognizing a convincing position once it is articulated (see Kerr, MacCoun, & Kramer, 1996; Laughlin, 2011).1 But note that the winning argument has to evoke a conceptual scheme that strongly favors it, and the conceptual scheme has to be broadly shared for ‘‘Truth Wins’’ to work. ‘‘Truth Wins’’ can also be distinguished from prestige-based influence (French & Raven, 1960; Henrich, 2000). In prestige-based influence, a sole advocate can have a disproportionate impact, but only if he or she has prestigious traits (reputation, maturity, status, a good track record for accuracy). bBOP could be modified to apply prestige weights to each source, but given the model’s extremely good fit to data the added complexity and loss of parsimony seem unnecessary.
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کلمات کلیدی:
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