  Information is often stored in a distributed and proprietary form, and agents
who own information are often self-interested and require incentives to reveal
their information. Suitable mechanisms are required to elicit and aggregate
such distributed information for decision making. In this paper, we use
simulations to investigate the use of decision markets as mechanisms in a
multi-agent learning system to aggregate distributed information for
decision-making in a contextual bandit problem. The system utilises strictly
proper decision scoring rules to assess the accuracy of probabilistic reports
from agents, which allows agents to learn to solve the contextual bandit
problem jointly. Our simulations show that our multi-agent system with
distributed information can be trained as efficiently as a centralised
counterpart with a single agent that receives all information. Moreover, we use
our system to investigate scenarios with deterministic decision scoring rules
which are not incentive compatible. We observe the emergence of more complex
dynamics with manipulative behaviour, which agrees with existing theoretical
analyses.
