The evolutionary role of toughness in bargaining

Heifetz, Aviad; Segev, Ella

doi:10.1016/j.geb.2003.11.001

Cited by 42 publications

(34 citation statements)

References 38 publications

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“…The arguably most prominent account of this type is loss aversion (Kahneman et al, 1990), which posits that buyers and sellers differ in their sensitivity to the magnitude of an object's potential consequences (see below for details). Further, there is evidence in valuations of risky options that buyers and sellers differ in probability weighting (e.g., Brenner, Griffin, & Koehler, 2012), and some authors have highlighted the role of strategic misrepresentation (e.g., Heifetz & Segev, 2004;Isoni, Loomes, & Sugden, 2011;Plott & Zeiler, 2005, 2007. Finally, Yechiam and Hochman (2013a) have recently proposed a loss-attention account, according to which buyers and sellers may differ in the amount of attention they invest in a task.…”

Section: Abstract Computational Modeling Endowment Effect Risky Dmentioning

confidence: 99%

Unpacking buyer-seller differences in valuation from experience: A cognitive modeling approach

Pachur

Scheibehenne

2017

Psychon Bull Rev

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People often indicate a higher price for an object when they own it (i.e., as sellers) than when they do not (i.e., as buyers)-a phenomenon known as the endowment effect. We develop a cognitive modeling approach to formalize, disentangle, and compare alternative psychological accounts (e.g., loss aversion, loss attention, strategic misrepresentation) of such buyer-seller differences in pricing decisions of monetary lotteries. To also be able to test possible buyer-seller differences in memory and learning, we study pricing decisions from experience, obtained with the sampling paradigm, where people learn about a lottery's payoff distribution from sequential sampling. We first formalize different accounts as models within three computational frameworks (reinforcement learning, instance-based learning theory, and cumulative prospect theory), and then fit the models to empirical selling and buying prices. In Study 1 (a reanalysis of published data with hypothetical decisions), models assuming buyer-seller differences in response bias (implementing a strategicmisrepresentation account) performed best; models assuming buyer-seller differences in choice sensitivity or memory (implementing a loss-attention account) generally fared worst. In a new experiment involving incentivized decisions (Study 2), models assuming buyer-seller differences in both outcome sensitivity (as proposed by a loss-aversion account) and response bias performed best. In both Study 1 and 2, the models implemented in cumulative prospect theory performed best. Model recovery studies validated our cognitive modeling approach, showing that the models can be distinguished rather well. In summary, our analysis supports a loss-aversion account of the endowment effect, but also reveals a substantial contribution of simple response bias. Keywords Computational modeling . Endowment effect . Risky decision making . Decision from experience . Loss aversionThe minimum price at which people are willing to accept to sell an object they own is often higher than the maximum price they are willing to pay for the same object when they do not own it. This systematic difference between buying and selling prices-which can also occur within the same person when taking different perspectives-is known as the endowment effect (Kahneman, Knetsch, & Thaler, 1990, 1991Thaler, 1980; see also Birnbaum & Stegner, 1979). The endowment effect has attracted considerable attention because it contradicts a central tenet of economic theory: According to the Coase theorem, the value of an object should be independent of its initial ownership (Kahneman et al., 1990).There have been several proposals as to which cognitive mechanisms give rise to the endowment effect, or how buyers' and sellers' cognitive mechanisms might differ generally.1 For instance, there may be asymmetries between buyers and sellers in terms of the order in which they search for positive 1 Note that this issue is orthogonal to the question of which aspects of being endowed-such as ownership, expectations...

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Section: Abstract Computational Modeling Endowment Effect Risky Dmentioning

confidence: 99%

Unpacking buyer-seller differences in valuation from experience: A cognitive modeling approach

Pachur

Scheibehenne

2017

Psychon Bull Rev

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“…1 2 In another important class of examples, Heifetz and Segev (2004) use these results to study the dynamic emergence of "tough" behavior in a salient class of bargaining mechanisms under asymmetric information.…”

Section: Applicationsmentioning

confidence: 99%

The Dynamic Evolution of Preferences

2006

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This paper develops a general methodology for characterizing the dynamic evolution of preferences in a wide class of strategic interactions. We give simple conditions characterizing the limiting distribution of preferences in general games, and apply our results to study the evolutionary emergence of overconfidence and interdependent preferences. We also show that this methodology can be adapted to cases where preferences are only imperfectly observed.

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“…12 In specific cases, however, there may be more natural candidates for such selections; see for example the analysis in Section 3 below and in Heifetz and Segev (2003). 13 A set E ⊂ Y is universally measurable if for every Borel measure η on Y , E belongs to the completion with respect to η of the sigma algebra of Borel sets.…”

Section: All Games and Dispositionsmentioning

confidence: 99%

“…1 For a brief overview of this literature, see Samuelson (2001). Examples include Güth and Yaari (1992), Huck and Oechssler (1999), Weiss (1997, 1998), Fershtman and Heifetz (2002), Rotemberg (1994), Bester and Güth (1998), Possajennikov (2000), Bolle (2000), Bergman and Bergman (2000), Koçkesen, Ok, andSethi (2000a, 2000b), Guttman (2000), Sethi and Somanathan (2001), Kyle and Wang (1997), Benos (1998), Heifetz and Segev (2003), and Heifetz, Segev and Talley (2004).…”

Section: Introductionmentioning

confidence: 99%

What To Maximize If You Must

2003

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The assumption that decision makers choose actions to maximize their preferences is a central tenet in economics. This assumption is often justified either formally or informally by appealing to evolutionary arguments. In contrast, we show that in almost every game and for almost every family of distortions of a player's actual payoffs, some degree of this distortion is beneficial to the player because of the resulting effect on opponents' play. Consequently, such distortions will not be driven out by any evolutionary process involving payoff-monotonic selection dynamics, in which agents with higher actual payoffs proliferate at the expense of less successful agents. In particular, under any such selection dynamics, the population will not converge to payoff-maximizing behavior. We also show that payoff-maximizing behavior need not prevail even when preferences are imperfectly observed.

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The evolutionary role of toughness in bargaining

Cited by 42 publications

References 38 publications

Unpacking buyer-seller differences in valuation from experience: A cognitive modeling approach

Unpacking buyer-seller differences in valuation from experience: A cognitive modeling approach

The Dynamic Evolution of Preferences

What To Maximize If You Must

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