In the preceding item in his blog I discussed emergence, where elements produce wholes with properties not present in the elements. Emergence is studied as ‘adaptation’ in the research field of Complex Adaptive Systems (CAS). A subfield of that is that of Agent-Based Computational Models (ABCM). There, interaction and adaptation of agents is simulated in computational models. I have used that, with a PhD candidate and a postdoctoral researcher, to study whether and how trust can arise in markets.[i]
In general, such a model has at least the following elements: properties of agents (such as capabilities, preferences), a representation they make of relevant elements in their environment, rules for decision making, a mechanism whereby they observe and evaluate each other, adaptation, i.e. strengthening or weakening of rules and preferences, depending on perceived success, and the invention of new rules.
In this case the central question was under which circumstances, if at all, trust can emerge in markets, even while profit is the criterion of success, and agents can choose between competition and collaboration. They form an opinion on the trustworthiness of partners on the basis of loyalty in collaboration. Next to profit, trust may form part of the value of collaboration. The weight attached to trust relative to profit is adaptive, depending on realised profit. Their own trustworthiness is also adaptive. It is modelled as a threshold of defecting from a relationship: the higher the threshold, he greater the loyalty. In adaptation there is also a random element.
De model enabled us to investigate when and how frequently trust may grow even though success is measured as profit. The aim was to test claims from economics that under competition trust cannot survive. According to the simulations with the model, often trust does indeed grow, but it depends on the circumstances, governed by the settings of parameters of the model.
Beyond this modelling, here I give a reflection on traits that help adaptiveness. If through the uncertainty of emergence it is not possible to determine ahead of time what may happen, then one must be prepared for the unexpected. There are several ways for his.
In robustness one chooses a way that is not sensitive to unexpected turns. Then one may lose some benefit in some cases but avoids heavy loss in others. Robustness can be explored in scenario analysis. There, one invents different possible futures (scenarios) to investigate how sensitive options are to differences between them.
In flexibility one choses a way that can easily and quickly be replaced by another, to adapt to circumstances as they arise.
In resilience one is resistant, able to incur and absorb adversity. One form of that it is create slack: excess capacity to absorb unforeseeable shocks, in money, time, space, reputation, or cognitive capacity.
In Inventiveness one learns to learn, to invent new ways, depending on experience, and to analyse the conduct of others for their success, and to deliberately seek novel challenges by which one can discover new ways. That is found in the theory of invention that I discussed earlier in this blog (items 31, 35).
Diversity is important for the evolution of a group (such as a species, in evolution, or an industry, in markets), and for discovery. It increases the chance that at least ome of the various forms fits whatever emerges.
[i] See: T. Klos & B. Nooteboom, Agent-based computational transaction cost economics,
Journal of Economic Dynamics and Control, 25 (2001): 503-526, Alexander Gorobets & Bart
Nooteboom, Adaptive build-up and breakdown of trust: An agent-based computational approach,
Journal of management and Governance,10 (2006): 277-306.