Browsing by Author "Wang, Wenlong"

Now showing 1 - 1 of 1
  • Thumbnail Image
    Item
    Decision markets implementations for human forecasters and multi-agent learning systems : a thesis presented in partial fulfilment of the requirements for the degree of Doctor of Philosophy in Computer Science at Massey University, Albany, New Zealand
    (Massey University, 2023) Wang, Wenlong
    Mechanisms of collective decision-making are an increasingly important topic, given that relevant data and information are often distributed. Collective decision-making processes involve eliciting information from multiple agents, aggregating the information, and mapping the aggregated information to a decision. An obstacle to these processes is that information is often proprietary, held by self-interested agents, and sometimes even too sensitive to share. Decision markets are mechanisms for eliciting and aggregating such information into predictions for decision-making. A design for decision markets put forward by Chen, Kash, Ruberry, et al. uses prediction markets to elicit and aggregate predictions that are conditional to the available actions, and then uses a stochastic decision rule to determine, based on the aggregated forecasts, which action to select. The design is incentive-compatible and uses a decision scoring rule to evaluate and incentivise the self-interested agents for their forecasts. The first part of this thesis (Chapter 2) describes a framework for security-based decision markets that allows agents to make predictions by trading assets. Security-based decision markets are designed to be user-friendly for participants familiar with trading in stock markets. For prediction markets, such a framework is well studied. For decision markets, my results show there are important differences between scoring rule based and securities-based implementation. The second and third parts of this thesis (Chapters 3 and 4) investigate decision markets as mechanisms of collective decision-making for multi-agent learning problems, thus building a bridge between economic mechanisms and artificial intelligence. Chapter 3 provides a decision market based algorithm that allows a principal to train multiple autonomous agents with independent and identically distributed (iid) information to solve a contextual bandit problem. Simulation results demonstrate that the proposed multi-agent systems can achieve performance equivalent to a centralised counterpart without requiring direct access to the agents' iid information, which is necessary for the centralised counterpart. Chapter 4 describes a set of mechanisms that allow avoiding stochastic decision rules to select actions based on aggregated forecasts. This is important because committing to a stochastic (i.e., randomising) decision rule means that sometimes suboptimal decisions have to be taken. The mechanisms outlined in this chapter require agents to collectively predict a proxy instead of conditional outcomes. Simulations show that the performance is as good as a Bayesian model with access to all distributed information.