The task of designing effective economic and political institutions requires substantial foresight. The designer must anticipate not only the behavior of individual actors, but also how that behavior will aggregate. Rising complexity brought about by increases in speeds of adaptation, diversity, connectedness, and interdependence make institutional design all the more challenging. Given the focus on equilibria, the extant literature on mechanism design might appear incapable of coping with this complexity. Yet, I suggest that a deeper engagement with the origins of (...) the mechanism-design framework reveals insights that may help us design robust, adaptive institutions that can harness complexity. (shrink)

The economic crisis happening across the world over the last few years describes a range of interdependencies and interactions,and has highlighted the fundamentalf laws of neoclassical economic theory: its unedifying focus on prediction and, above all, its inability to explain how the economy really works. As such, it is increasingly recognised that economic phenomena cannot be exclusively investigated as being derived from deterministic, predictable and mechanistic dynamics. Instead, a new approach is required by which history-dependence, organic and ever-evolving processes are (...) also accounted for. As this view implies new challenges and opportunities for policy, we will focus our attention on innovative components of Complexity Theory for the study of economics and the evaluation of public policies. (shrink)

Algorithm Decision Making (ADM) systems designed to augment or automate human decision-making have the potential to produce better decisions while also freeing up human time and attention for other pursuits. For this potential to be realised, however, algorithmic decisions must be sufficiently aligned with human goals and interests. We take a Principal-Agent (P-A) approach to the questions of ADM alignment and trust. In a broad sense, ADM is beneficial if and only if human principals can trust algorithmic agents to act (...) faithfully on their behalf. This mirrors the challenge of facilitating P-A relationships among humans, but the peculiar nature of human-machine interaction also raises unique issues. The problem of asymmetric information is omnipresent but takes a different form in the context of ADM. Although the decision-making machinery of an algorithmic agent can in principle be laid bare for all to see, the sheer complexity of ADM systems based on deep learning models prevents straightforward monitoring. We draw on literature from economics and political science to argue that the problem of trust in ADM systems should be addressed at the level of institutions. Although the dyadic relationship between human principals and algorithmic agents is our ultimate concern, cooperation at this level must rest against an institutional environment which allows humans to effectively evaluate and choose among algorithmic alternatives. (shrink)