An official website of the United States government
The concept of TRUSTWORTHINESS plays a role in the formation, maintenance, and dissolution of friendships, marriages, and cooperative relationships from small to large scales. Here, we analyze TRUSTWORTHINESS under the assumption that such concepts evolved to guide action adaptively. Intuition and research suggest that actors trust targets who have not engaged in betrayals. However, this perspective fails to capture certain real-world behaviors (e.g., when two people cheating on their spouses enter a relationship with each other and expect mutual fidelity). Evolutionary task analysis suggests that TRUSTWORTHINESS is structured to help actors address challenges of extending trust, where actors may gain or lose from doing so. In six experiments with American adults (N=1,718), we test the hypothesis that TRUSTWORTHINESS tracks not only (i) whether targets refrain from betraying trust when given opportunities, but also (ii) the impact of betrayal on the actor. Data generally support this hypothesis across relationships (friendships, romantic, professional): Actors deem non-betrayers more trustworthy than betrayers, but also deem betrayers more trustworthy when betrayals benefit actors. TRUSTWORTHINESS may incline actors to trust to those who refrain from betraying others—a potent signal of reluctance to betray oneself—while also favoring those who betray others if it serves oneself.
more »
« less
Impression management attenuates the effect of ability on trust in economic exchange
Are competent actors still trusted when they promote themselves? The answer to this question could have far-reaching implications for understanding trust production in a variety of economic exchange settings in which ability and impression management play vital roles, from succeeding in one’s job to excelling in the sales of goods and services. Much social science research assumes an unconditional positive impact of an actor’s ability on the trust placed in that actor: in other words, competence breeds trust. In this report, however, we challenge this assumption. Across a series of experiments, we manipulated both the ability and the self-promotion of a trustee and measured the level of trust received. Employing both online laboratory studies ( n = 5,606) and a field experiment ( n = 101,520), we find that impression management tactics (i.e., self-promotion and intimidation) can substantially backfire, at least for those with high ability. An explanation for this effect is encapsuled in attribution theory, which argues that capable actors are held to higher standards in terms of how kind and honest they are expected to be. Consistent with our social attribution account, mediation analyses show that competence combined with self-promotion decreases the trustee’s perceived benevolence and integrity and, in turn, the level of trust placed in that actor.
more »
« less
- Award ID(s):
- 1943688
- PAR ID:
- 10412721
- Date Published:
- Journal Name:
- Proceedings of the National Academy of Sciences
- Volume:
- 119
- Issue:
- 30
- ISSN:
- 0027-8424
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
null (Ed.)Absatract Actors collaborate via message exchanges to reach a common goal. Experience has shown, however, that pure message-based communication is limiting and forces developers to use design patterns. The recently introduced dataspace actor model borrows ideas from the tuple space realm. It offers a tightly controlled, shared storage facility for groups of actors. In this model, actors assert facts that they wish to share and interests in such assertions. The dataspace notifies interested parties of changes to the set of assertions that they are interested in. Although it is straightforward to add the dataspace model to untyped languages, adding a typed interface is both necessary and challenging. Without restrictions on exchanged data, a faulty actor may propagate erroneous data through a malformed assertion, causing an otherwise well-behaved actor to crash—violating the key principle of failure isolation. A properly designed type system can prevent this scenario and rule out other kinds of uncooperative actors. This paper presents the first structural type system for the dataspace model of actors; it does not address the question of behavioral types for assertion-oriented protocols.more » « less
-
Abstract Reducing tillage is a key goal for conservation and regenerative agriculture, yet research has struggled to identify ways to increase the use of the practice among farmers. Recent scholarship has identified social capital as an important piece of the adoption puzzle. However, the ways in which farmers' social capital influences conservation practice use are seldom identified or explored. In this study, we tested the effects of three measures of social capital on the adoption of no‐till among 1,523 row crop farmers in the United States Corn Belt. Specifically, we operationalized the extent to which farmers' social networks, network trust, and community conservation norms affect intra‐individual processes and thus influence farmers' decisions regarding adoption. Our results identified key mechanisms for the promotion of conservation practices through social capital. Subjective conservation norms emerged as a main pathway through which farmers' social capital influenced their use of no‐till, indicating that networks, network trust, and community norms can increase adoption through affective paths. We conclude that academic research and policy experts should continue to situate farmers as social actors and pay heed to the norms and cultural expectations surrounding agricultural conservation practices.more » « less
-
Not AvailableActor systems are a flexible model of concurrent and distributed programming, which are efficiently implementable, and avoid many classic concurrency bugs by construction. However they must still deal with the challenge of messages arriving in unexpected orders. We describe an approach to restricting the orders in which actors send messages to each other, by equipping actor references—the handle used to address another actor—with a protocol restricting which message types can be sent to another actor and in which order using that particular actor reference. This endows the actor references with the properties of (flow-sensitive) static capabilities, which we call actor capabilities. By sending other actors only restricted actor references, actors may control which messages are sent in which orders by other actors. Rules for duplicating (splitting) actor references ensure that these restrictions apply even in the presence of delegation. The capabilities themselves restrict message send ordering, which may form the foundation for stronger forms of reasoning. We demonstrate this by layering an effect system over the base type system, where the relationships enforced between the actor capabilities and the effects of an actor’s behaviour ensure that an actor’s behaviour is always prepared to handle any message that may arrive.more » « less
-
Abstract Understanding network influence and its determinants are key challenges in political science and network analysis. Traditional latent variable models position actors within a social space based on network dependencies but often do not elucidate the underlying factors driving these interactions. To overcome this limitation, we propose the social influence regression (SIR) model, an extension of vector autoregression tailored for relational data that incorporates exogenous covariates into the estimation of influence patterns. The SIR model captures influence dynamics via a pair of$$n \times n$$matrices that quantify how the actions of one actor affect the future actions of another. This framework not only provides a statistical mechanism for explaining actor influence based on observable traits but also improves computational efficiency through an iterative block coordinate descent method. We showcase the SIR model’s capabilities by applying it to monthly conflict events between countries, using data from the Integrated Crisis Early Warning System (ICEWS). Our findings demonstrate the SIR model’s ability to elucidate complex influence patterns within networks by linking them to specific covariates. This paper’s main contributions are: (1) introducing a model that explains third-order dependencies through exogenous covariates and (2) offering an efficient estimation approach that scales effectively with large, complex networks.more » « less
- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1073/pnas.2118548119
