Dynamics of trust among entities in a mechanistic simulation model : with research implications for public organizations

We develop a mathematical model describing the dynamics of trust relationships among multiple entities. Those entities may represent individuals, businesses, or political organizations, or a combination of these. We model all reciprocal trust relationships, including self-trust (or internal trust) and examine the interaction of eight primary factors; actual performance, expected performance, variation in both actual and expected performance, tolerance thresholds, cooling period after performance failure, relational influence among organizations, and leadership clout. Exercising the model, we find that each of these factors has a different impact on the trust dynamics of the system as a whole. Some of the factors lead to system stability and others to system breakdown. Cycles of trust and distrust are observed. These results may have some implications for business and governmental organizations, and we outline some areas of future research that might improve their applicability and advance our understanding of these dynamics further. Herting and Hamon Simulation of trust dynamics 3 Background We describe an interactive computer model portraying the dynamics of trust and distrust in asymmetric organizational relationships over extended periods of time. The model is intended to be generic and scalable, and thus applicable to many different organizational trust scenarios, in government, business and society. A key feature is the simulation of cycles of slow accumulation and rapid disintegration of trust. We describe the rationale for the model, the details of its construction, the underlying mathematical equations, some results from exercise of the model and suggested implications for further research in the area of trust in government. To provide an operational context for the model, we invite the reader to consider the following three vignettes: 1. You approach a deep pool in a cool mountain stream. You can dimly make out a school of trout seemingly suspended in the flow of the stream, arrayed in parade formation, moving only their gills. Quietly you approach. A few trout twitch a fin or tail nervously, but remain in place. You make a slight noise and one of the trout swims away. You take a few more steps and a few of the fish seem to slowly break out of the formation, leaving the others in place. You toss a pebble into the pool and the fish frantically scatter in all directions. You silently wait by the vacated pool and, several long minutes later, a few trout reluctantly return, one by one. Several more minutes pass and you can now see that the entire school has taken up its original position. 2. For many years, every Friday you and your coworkers have stopped by the neighborhood bank to deposit some savings. Although the interest rates are low, you have now accumulated a retirement sum. You know the vice president of the bank and a few people on the board personally. You go to church with the loan officer. The tellers greet you by name. Several months ago you were a bit alarmed at news of a bank examiner’s investigation. Not thinking much of it, you continue to deposit savings each Friday. However, yesterday you read that a grand jury indicted the bank president and loan officer. Today, you stood in line with a noisy group of people, clutching their passbooks, demanding to withdraw their funds. When you finally get your money, all in cash, you take it home and hide it. Perhaps you will look for another bank. 3. The national government of your country maintains a large military force, based on a national system of conscription. Every youthful citizen must serve for two years, regardless. Military service is regarded as prestigious, often leading to government service positions. In recent years, however, your country has become involved in several military conflicts. There have been casualties. You have a son Herting and Hamon Simulation of trust dynamics 4 who is nearing the age of eligibility. You know that children of privileged families have avoided military service by studying abroad. Yesterday you heard of a violent attack on your country’s armed forces. You call the airlines. The phone is busy. When you connect, you are told of a new emigration law requiring special authorization to leave the country. You and your son pack bags and head for a remote border crossing. There is an unusual amount of traffic at the border. You say goodbye to your son. Perhaps he can return after the war is over. From these three very different contexts, a pattern emerges with the major components of our proposed model of trust dynamics: 1) A stable system of increasing trust, where actualities match or exceed expectations, coexisting with 2) tolerated incidents of distrust. The system is suddenly interrupted by 3) a precipitous breech of trust, resulting in 4) a massive system breakdown, followed by 5) a period of withdrawal, and, perhaps, 6) a slow return to a stable system trust. These factors are all exacerbated or mitigated by 7) asymmetries of power and 8) exchanges of influence and information, the whole process being reiterated in cycles of system behavior. We propose that this pattern of trust dynamics is observed on many levels, in personal relationships, community disputes, stock market crashes, political system failures, government collapses, civil disturbances and worse. Similar patterns of trust (cooperation) and defection emerge even in socio-biological systems (Axelrod 1984). Because large social calamities may be associated with the phenomena of trust, its study is important for stable governance. We are interested in the possibility that these patterns of trust and distrust can be modeled as a total network, and that simulated cycles of trust and distrust might effect simulated system breakdowns. We are also interested in the idea that cycles of trust and distrust could be influenced by intervention with any or all of the eight components. Our computer model draws inspiration from the Prisoner’s Dilemma models originally developed in the 1960’s (Axelrod 1984; Poundstone 1992). We are also intrigued with Sato’s work (2002) that describes a rational choice model of mutual trust, assuming innate characteristics of trustworthiness and untrustworthiness of actors and trustors. Like Sato, we are interested in further experiment with trust models applying evolutionary approaches and change in trustworthiness over time. Our model differs from the Prisoner’s Dilemma model most importantly in that it is more focused on the combined effects of multiple actors in a complex situation rather than individual strategies of any particular actor. Our model probably differs from the Sato (2002) model in that we are not emphasizing innate characteristics of Herting and Hamon Simulation of trust dynamics 5 individuals, but, rather, the results of many interactions of many individuals in a complex social network, analogous to a government/citizen framework of relationships. Concepts of trust We found some underpinnings for our model in literature concerning trust in organizations. Many writers point to trust as rational choice; others emphasize subjective, social or emotional aspects of trust. Trust has been described systemically as a fundamental ingredient for collaboration among organizations (e.g. Lewicki, McAllister and Bies 1998; Costigan, Ilter and Berman 1998; Luhmann 1982; Lewicki and Bunker 1995), with variations including fiduciary trust, mutual trust, social trust, characteristic-based trust, process-based trust, institutional-based trust (Thomas 1998), conditioned-based trust (Jones and George 1998), calculus-based trust, knowledge-based trust, identity-based trust, as well as trust as a product of expectations, trust as security, and trust on a personal as well as a systemic level (Shapiro et. al. 1992; Lewicki and Bunker 1995). We also find that the processes of trust and distrust may be fundamental even to biological processes. Many aspects of evolutionary biology deal with behaviors and characteristics that rely on, or result from, an accurate understanding of the relationships of individuals, the level to which individual behavior meets expectations, or the extent to which a signal is clearly indicative of the state of the signaler. The trustworthiness of biological signals is one of the most important factors affecting signal evolution in biological systems (KodricBrown and Brown 1984, Endler 1992). Many biological signals give an ‘honest’ indication of the condition or state of the signaler (e.g. Hamon et al. 1999; Collins and Missing 2003; Papeschi and Dessì-Fulgher 2003; Reby and McComb 2003). While many of these signals are part of the mating systems of animals, some prey species also apparently inspect potential predators to gauge their intentions and motivational state (Brown and Dreier 2003). The implication of these different aspects of biological signals is that signals must be trustworthy or they will not become prevalent, as organisms that rely on them are deceived and fail to survive or reproduce. The evolution of biological signals indicates that trust has implications for the long-term sustainability of social systems. Trust may be an organizational stability factor, “the level of confidence that one individual has in another to act in a fair, ethical, and predictable manner” (Nyhan and Marlow Herting and Hamon Simulation of trust dynamics 6 1997, p. 615), reducing the complexity of an environment by restricting possible outcomes to a smaller set of possibilities. It may be differentiated on a personal level from trust at a systems level (Nyhan and Marlow 1997). Hosmer (1995) framed organizational trust as mutually productive exchanges of resources that depended upon personal trust, as well as formal social mechanisms that are designed to ensure trustworthy conduct. For a definition of trust, we see trust, like Hardin (2000) and Zolin (2002), as a directional relationship between two parties, invo