Confidence as an expression of commitment: why misplaced expressions of confidence backfire

Vullioud, Colin; Clément, Fabrice; Scott‐Phillips, Thom; Mercier, Hugo

doi:10.1016/j.evolhumbehav.2016.06.002

Cited by 53 publications

(52 citation statements)

References 34 publications

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“…The rationale is that an initial investment commits one to a course of action, in this case benefiting from long term cooperation as opposed to taking the temptation to defect. One can predict that the greater the potential benefits from cooperative relationships, the greater the up-front cost that can be sustained in signalling commitment [compare recent work on commitment in language (Vullioud et al, 2017). However, the reputation-based partner choice model also differs from a model of ‘cheap talk’ where low cost signals can indicate intent provided there are repeated interactions (Silk, Kaldor, and Boyd 2000) in that it specifically explains costly reputation-building and in that reputations are the result of single signalling events rather than being learned over many rounds.…”

Section: Discussionmentioning

confidence: 99%

Honest signalling of trustworthiness

Roberts

2019

Preprint

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7 8 Trust can transform conflicting interests into cooperation. But how can 9 individuals know when to trust others? Here, I develop the theory that 10 reputation building may signal cooperative intent, or 'trustworthiness'. I model 11 a simple representation of this theory in which individuals (1) optionally invest 12 in a reputation by performing costly helpful behaviour ('signalling'); (2) 13 optionally use others' reputations when choosing a partner; and (3) optionally 14 cooperate with that partner. In evolutionary simulations, high levels of 15 reputation building; of choosing partners based on reputation; and of 16 cooperation within partnerships emerged. Costly helping behaviour evolved 17 into an honest signal of trustworthiness when it was adaptive for cooperators, 18 relative to defectors, to invest in the long-term benefits of a reputation for 19 helping. I show using game theory that this occurs when cooperators gain 20 larger marginal benefits from investing in signalling than do defectors. This 21 happens without the usual costly signalling assumption that individuals are of 22 two 'types' which differ in quality. Signalling of trustworthiness may help 23 explain phenomena such as philanthropy, pro-sociality, collective action, 24 punishment, and advertising in humans and may be particularly applicable to 25 courtship in other animals. 26 78or may not benefit one or more recipients of any benefit and that the receivers of the 79 signal may or may not be the recipients of such benefits. In contrast, indirect 80 reciprocity involves a donation of a benefit to a recipient at a cost to the actor. 81(3)Indirect reciprocity is explicitly conditional in that it can only operate where 82 individuals help those who help others. Reputation-based partner choice has no such 83 conditionality. Individuals help others as a signal of cooperativeness. They benefit 84 from then being more likely to be chosen for profitable partnerships. 85 (4) Reputation-based partner choice involves partner choice for profitable 86 relationships. The basic theory of indirect reciprocity involves no partner choice. It 87 can be extended only in as much as individuals could theoretically choose who to 88 give to. It cannot be extended to involve choosing as a partner someone that one has 89 given to since this would involve direct reciprocity, which is a different theory of how 90 helping Is beneficial. 91 The two theories have been compared experimentally and reputation-based partner 92 choice has been found to be more effective in inducing reputational concerns 93 (Sylwester and Roberts 2013). 94 95 The theory of reputation-based partner choice explicitly incorporates Zahavi's 96 Handicap Principle (Grafen 1990, Zahavi 1997), a form of costly signalling theory 97 (Gintis, Smith, and Bowles 2001) whereby the signal (cooperative reputation) reveals 98 any underlying differences in some quality such as resources. Inherent in this 99 approach is that reputations are more than simply histories of how individuals have 100 previously act...

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Section: Discussionmentioning

confidence: 99%

Honest signalling of trustworthiness

Roberts

2019

Preprint

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“…Belief value is reasonable because that a worker always intentionally or unintentionally estimates in her own mind the probability of giving the right answer when she is encountered with a decision making task due to tasks' inherent difficulty, different individual experience and professional knowledge, etc. [23], [24].…”

Section: Reward-penalty Modelmentioning

confidence: 99%

“…of which the first means that a worker must commit c to maximize her expected gain, i.e., a worker can get the largest expected gain payment just only if she chooses to commit the truth [25], [26]; the second means that the reward and penalty have a positive correlation with x, a larger belief value of its corresponding committing type has a larger influence on the benchmark and final answer of the task, and it may be beneficial or harmful [23], [24]; the third means the boundary values, and the fourth means the definitional domain. A fixed boundary value is to ensure easy solvability of reward-penalty functions [27].…”

Section: B Reward-penalty Modulementioning

confidence: 99%

A Blockchain-Enabled Trustless Crowd-Intelligence Ecosystem on Mobile Edge Computing

Wang

Bhargava

et al. 2019

IEEE Trans. Ind. Inf.

105

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Crowd-intelligence tries to gather, process, infer and ascertain massive useful information by utilizing the intelligence of crowds or distributed computers, which has great potential in Industrial Internet of Things (IIoT). A crowd-intelligence ecosystem involves three stakeholders, namely the platform, workers (e.g., individuals, sensors or processors), and task publisher. The stakeholders have no mutual trust but interest conflict, which means bad cooperation of them. Due to lack of trust, transferring raw data (e.g., pictures or video clips) between publisher and workers requires the remote platform center to serve as a relay node, which implies network congestion. First we use a rewardpenalty model to align the incentives of stakeholders. Then the predefined rules are implemented using blockchain smart contract on many edge servers of the mobile edge computing network, which together function as a trustless hybrid humanmachine crowd-intelligence platform. As edge servers are near to workers and publisher, network congestion can be effectively improved. Further, we proved the existence of the only one strong Nash equilibrium, which can maximize the interests of involved edge servers and make the ecosystem bigger. Theoretical analysis and experiments validate the proposed method respectively.Index Terms-Mobile edge computing, blockchain smart contract, crowd-intelligence ecosystem, trustless, hybrid humanmachine, reward and penalty, strong Nash equilibrium.

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“…One might also see the illusion of explanatory depth as a form of overconfidence: being overly confident in our ability to explain something. Just as metacognition, overconfidence has been cast as having a social function: to impress and influence others (Anderson, Brion, Moore, & Kennedy, 2012;Vullioud, Clément, Scott-Phillips, & Mercier, 2016), a possibility that the authors merely evoke in passing.…”

Section: View-why Bother?mentioning

confidence: 99%

“…It can also backfire in two cases: if it turns out to be unfounded, or if it can be taken to challenge claims of competence of people in position of authority.Thus an evolutionary perspective, with the place it gives to costs and benefits and to competition, may help throw further light on the illusion of knowledge and its relationship to the social sharing of knowledge that are at the center of Sloman and Fernbach's book. One of the main mechanisms that the audience relies on to keep communicators mostly honest is to keep track of the content and strength of their commitments (e.g Vullioud et al, 2016)…”

mentioning

confidence: 99%