Trade-based Asset Model using Dynamic Junction Tree for Combinatorial Prediction Markets

TL;DR

This paper introduces the Dynamic Asset Cluster (DAC), a novel system for managing combinatorial prediction market assets efficiently using dynamic junction trees, enabling better scalability and performance.

Contribution

The paper presents a new asset management system that improves time and space efficiency for combinatorial prediction markets through dynamic junction trees and asset blocks.

Findings

Enhanced efficiency in asset management

Dynamic junction trees reduce computational overhead

Supports complex combinatorial forecasts

Abstract

Prediction markets have demonstrated their value for aggregating collective expertise. Combinatorial prediction markets allow forecasts not only on base events, but also on conditional and/or Boolean combinations of events. We describe a trade-based combinatorial prediction market asset management system, called Dynamic Asset Cluster (DAC), that improves both time and space efficiency over the method of, which maintains parallel junction trees for assets and probabilities. The basic data structure is the asset block, which compactly represents a set of trades made by a user. A user's asset model consists of a set of asset blocks representing the user's entire trade history. A junction tree is created dynamically from the asset blocks to compute a user's minimum and expected assets.