In psychology and sociology, a trust metric is a measurement or metric of the degree to which one social actor (an individual or a group) trusts another social actor. Trust metrics may be abstracted in a manner that can be implemented on computers, making them of interest for the study and engineering of virtual communities, such as Friendster and LiveJournal.
Trust escapes a simple measurement because its meaning is too subjective for universally reliable metrics, and the fact that it is a mental process, unavailable to instruments. There is a strong argumentCastelfranchi, C. and Falcone, R. (2000) Trust is much more than subjective probability: Mental components and sources of trust. Proc. of the 33rd Hawaii Int. Conf. on System Sciences (HICSS2000). Vol. 6. against the use of simplistic metrics to measure trust due to the complexity of the process and the 'embeddedness' of trust that makes it impossible to isolate trust from related factors.
There is no generally agreed set of properties that make a particular trust metric better than others, as each metric is designed to serve different purposes, e.g.Ziegler, C.-N., and Lausen, G. (2005) Propagation Models for Trust and Distrust in Social Networks. Inf. Syst. Frontiers vol. 7, no. 4–5, pp. 337–358 provides certain classification scheme for trust metrics. Two groups of trust metrics can be identified: Empirical metrics focusing on supporting the capture of values of trust in a reliable and standardized way; Formal metrics that focus on formalization leading to the ease of manipulation, processing and reasoning about trust. Formal metrics can be further classified depending on their properties.
Trust metrics enable trust modellingMarsh, S. P. (1994) Formalising Trust as a Computational Concept. University of Stirling PhD thesis. and reasoning about trust. They are closely related to reputation systems. Simple forms of binary trust metrics can be found e.g. in PGP.Zimmermann, P. (1993) Pretty Good Privacy User's Guide, Volume I and II. Distributed with the PGP software The first commercial forms of trust metrics in computer software were in applications like eBay's Feedback Rating. Slashdot introduced its notion of karma, earned for activities perceived to promote group effectiveness, an approach that has been very influential in later virtual communities.
Empirical metrics
Empirical metrics capture the value of trust by exploring the behavior or introspection of people, to determine the perceived or expressed level of trust. Those methods combine theoretical background (determining what it is that they measure) with defined set of questions and statistical processing of results.
The willingness to cooperate, as well as actual cooperation, are commonly used to both demonstrate and measure trust. The actual value (level of trust and/or trustworthiness) is assessed from the difference between observed and hypothetical behaviors i.e. those that would have been anticipated in the absence of cooperation.
Surveys
Surveys capture the level of trust by means of both observations or introspection, but without engaging into any experiments. Respondents are usually providing answers to a set of questions or statements and responses are e.g. structured according to a Likert scale. Differentiating factors are the underlying theoretical background and contextual relevance.
One of the earliest surveys are McCroskey's scalesJames C. McCroskey, J. C. (1966) Scales for the Measurement of Ethos. Speech Monographs, 33, 65–72. that have been used to determine authoritativeness (competence) and character (trustworthiness) of speakers. Rempel's trust scaleRempel, J. K., Holmes, J. G. and Zanna, M. P. (1985): Trust in close relationships. Journal of Personality and Social Psychology. vol. 49 no. 1, pp. 95–112. 1985. and Rotter's scaleRotter, J. B. (1971) Generalized expectancies for interpersonal trust. American Psychologist, vol. 26 no. 5 pp. 443–52. are quite popular in determining the level of interpersonal trust in different settings. The Organizational Trust Inventory (OTI)Cummings, L. L., and Bromiley, P. (1996) The Organizational Trust Inventory (OTI): Development and Validation. In: Kramer, R. M. and Tyler, T. R.: Trust in Organizations. Sage Publications. is an example of an exhaustive, theory-driven survey that can be used to determine the level of trust within the organisation.
For a particular research area a more specific survey can be developed. For example, the interdisciplinary model of trust,McKnight, D. H., Chervany, N. L. (2001) Conceptualizing Trust: A Typology and E-Commerce Customer Relationships Model. Proc. of the 34th Hawaii Int. Conf. on System Sciences has been verified using a survey whileCorritore, C. L. et al (2005) Measuring Online Trust of Websites: Credibility, Perceived Ease of Use, and Risk. In: Proc. of Eleventh Americas Conf. on Information Systems, Omaha, NE, USA pp. 2419–2427. uses a survey to establish the relationship between design elements of the web site and perceived trustworthiness of it.
Games
Another empirical method to measure trust is to engage participants in experiments, treating the outcome of such experiments as estimates of trust. Several games and game-like scenarios have been tried, some of which estimate trust or confidence in monetary terms (seeKeser, C. (2003) Experimental games for the design of reputation management systems. IBM Systems J., vol. 42, no. 3. Article for an interesting overview).
Games of trust are designed in a way that their Nash equilibrium differ from Pareto optimum so that no player alone can maximize their own utility by altering his selfish strategy without cooperation, while cooperating partners can benefit. Trust can be therefore estimated on the basis of monetary gain attributable to cooperation.
The original 'game of trust' has been described inBerg, J., Dickhaut, J., and McCabe, K. (1995) Trust, Reciprocity, and Social History, Games and Economic Behavior 10, 122–142 as an abstracted investment game between an investor and his broker. The game can be played once or several times, between randomly chosen players or in pairs that know each other, yielding different results.
Several variants of the game exist, focusing on different aspects of trust as the observable behaviour. For example, rules of the game can be reversed into what can be called a game of distrust,Bohnet, I., and Meier, S. (2005) Deciding to distrust. KSG Working Paper No. RWP05-049. declaratory phase can be introducedAiriau, S., and Sen, S. (2006) Learning to Commit in Repeated Games. In: Proc. of the Fifth Int. Joint Conf. on Autonomous Agents and Multiagent Systems (AAMAS06). or rules can be presented in a variety of ways, altering the perception of participants.
Other interesting games are e.g. binary-choice trust games,Camerer, C., and Weigelt, K. (1988) Experimental Tests of a Sequential Equilibrium Reputation Model. Econometrica 56(1), pp. 1–36. the gift-exchange game,Fehr, E., Kirchsteiger, G., and Riedl, A. (1993) Does Fairness Prevent Market Clearing? An Experimental Investigation. Quarterly Journal of Economics 108(May), pp. 437–60. cooperative trust games, and various other forms of social games. Specifically the Prisoners DilemmaPoundstone, W. (1992) Prisoner's Dilemma. Doubleday, NY. 1992 are popularly used to link trust with economic utility and demonstrate the rationality behind reciprocity. For multi-player games, different forms of close market simulations exist.Bolton, G. E., Elena Katok, E., and Ockenfels, A. (2003) How Effective are Electronic Reputation Mechanisms? An Experimental Investigation.
Formal metrics
Formal metrics focus on facilitating trust modelling, specifically for large scale models that represent trust as an abstract system (e.g. social network or web of trust). Consequently, they may provide weaker insight into the psychology of trust, or in particulars of empirical data collection. Formal metrics tend to have a strong foundations in algebra, probability or logic.
Representation
There is no widely recognised way to attribute value to the level of trust, with each representation of a 'trust value' claiming certain advantages and disadvantages. There are systems that assume only binary values,Adams, C., and Lloyd, S. (2002) Understanding PKI: Concepts, Standards, and Deployment Considerations. Sams. that use fixed scale,Zimmermann, P. (ed.) (1994) PGP User's Guide. MIT Press, Cambridge. where confidence range from −100 to +100 (while excluding zero),Tyrone Grandison, T. (2003) Trust Management for Internet Applications. PhD thesis, University of London, UK. from 0 to 1Mui, L. et al. (2002) A Computational Model of Trust and Reputation. 35th Hawaii Int. Conf. on System Science (HICSS).Richters, O., Peixoto. T.P. (2011) Trust Transitivity in Social Networks. PLoS ONE 6(4): e18384. or from [−1 to +1);Marsh, S. P. (1994) Formalising Trust as a Computational Concept. University of Stirling PhD thesis. where confidence is discrete or continuous, one-dimensional or have many dimensions.Gujral, N., DeAngelis, D., Fullam, K. K., and Barber, K. S. (2006) Modelling Multi-Dimensional Trust. In: Proc. of Fifth Int. Conf. on Autonomous Agents and Multiagent Systems AAMAS-06. Hakodate, Japan. Some metrics use ordered set of values without attempting to convert them to any particular numerical range (e.g.Nielsen, M. and Krukow, K. (2004) On the Formal Modelling of Trust in Reputation-Based Systems. In: Karhumaki, J. et al. (Eds.): Theory Is Forever, Essays Dedicated to Arto Salomaa on the Occasion of His 70th Birthday. Lecture Notes in Computer Science 3113 Springer. SeeAbdul-Rahman, A. (2005) A Framework for Decentralised trust Reasoning. PhD Thesis. for a detailed overview).
There is also a disagreement about the semantics of some values. The disagreement regarding the attribution of values to levels of trust is specifically visible when it comes to the meaning of zero and to negative values. For example, zero may indicate either the lack of trust (but not distrust), or lack of information, or a deep distrust. Negative values, if allowed, usually indicate distrust, but there is a doubtCofta, P. (2006) Distrust. In: Proc. of Eight Int. Conf. on Electronic Commerce ICEC'06, Fredericton, Canada. pp. 250–258. whether distrust is simply trust with a negative sign, or a phenomenon of its own.
Subjective probability
Subjective probabilityGambetta, D. (2000) Can We Trust Trust? In: Gambetta, D. (ed.) Trust: Making and Breaking Cooperative Relations, electronic edition, Department of Sociology, University of Oxford, chapter 13, pp. 213–237, focuses on trustor's self-assessment about his trust in the trustee. Such an assessment can be framed as an anticipation regarding future behaviour of the trustee, and expressed in terms of probability. Such a probability is subjective as it is specific to the given trustor, their assessment of the situation, information available to him etc. In the same situation other trustors may have a different level of a subjective probability.
Subjective probability creates a valuable link between formalisation and empirical experimentation. Formally, subjective probability can benefit from available tools of probability and statistics. Empirically, subjective probability can be measured through one-side bets. Assuming that the potential gain is fixed, the amount that a person bets can be used to estimate his subjective probability of a transaction.
Uncertain probabilities (subjective logic)