Risk Dynamics in Trade Networks
This work provides a theoretical framework for analyzing risk dynamics in trade networks, with applications in prediction markets and agent-based systems, though it appears incremental by building on existing coordinate descent methods.
The authors tackled the problem of modeling risk-minimizing trade interactions among agents using financial risk measures, showing that their trade dynamics correspond to randomized coordinate descent and converge to a unique steady state with proven rates.
We introduce a new framework to model interactions among agents which seek to trade to minimize their risk with respect to some future outcome. We quantify this risk using the concept of risk measures from finance, and introduce a class of trade dynamics which allow agents to trade contracts contingent upon the future outcome. We then show that these trade dynamics exactly correspond to a variant of randomized coordinate descent. By extending the analysis of these coordinate descent methods to account for our more organic setting, we are able to show convergence rates for very general trade dynamics, showing that the market or network converges to a unique steady state. Applying these results to prediction markets, we expand on recent results by adding convergence rates and general aggregation properties. Finally, we illustrate the generality of our framework by applying it to agent interactions on a scale-free network.