With Gabriele Camera and Gary Charness
R&R Management Science
Abstract:
The future architecture of financial systems is a subject of contention, with centralized and decentralized governance proponents. Here we ask: would the architecture affect the quality of decision-making? We propose a game where financial network participants demarcate the ownership of claims to income. This governance task can be decentralized (shared authority), centralized (single authority), or hybrid (alternating authority). Without communication all architectures supported poor outcomes. With communication, decentralization ensured good governance and maximum profits, while centralization did not—lowering communication’s potency in promoting socially optimal decisions. This indicates there is scope for decentralization in innovating financial institutions.
With Charles Johnson, Brian Jabarian, Enoch Yeung and Gary Charness
Working Paper
Abstract:
Abstract. We propose a distributional framework for eliciting risk preferences that treats an individual's attitude towards risk as a complete probability distribution rather than a point estimate. By parameterizing preferences with the flexible beta family, our approach encompasses the entire spectrum from extreme risk aversion to risk neutrality and even risk-seeking behavior, while simultaneously allowing for heterogeneous stability of those attitudes across contexts. Our agent-based simulations show that (i) the true underlying preference distribution is recoverable with negligible bias, and (ii) the precision of recovery is a systematic function of the elicitation design richness, providing clear guidance for experimental design. Benchmarking on the comprehensive laboratory dataset of Holzmeister & Schmidt (2021) confirms two central results: (1) the out-of-sample predictive accuracy is at least on par with the canonical point estimation methods, and (2) our method delivers a second, policy-relevant moment, the subject-specific variance of risk taking, without sacrificing parsimony.
With Robert McLaughlin, Dingyue Liu and Dahlia Malkhi
Working Paper
Abstract:
Trading on decentralized exchanges via an Automated Market Maker (AMM) mechanism has been massively adopted, with a daily trading volume reaching $1B. This trading method has also received close attention from researchers, central banks, and financial firms, who have the potential to adopt it to traditional financial markets such as foreign exchanges and stock markets. A critical challenge of AMM-powered trading is that transaction order has high financial value, so a policy or method to order transactions in a "good" (optimal) manner is vital. We offer economic measures of both price stability (low volatility) and inequality that inform how a "social planner" should pick an optimal ordering. We show that there is a trade-off between achieving price stability and reducing inequality, and that policymakers must choose which to prioritize. In addition, picking the optimal order can often be costly, especially when performing an exhaustive search over trade orderings (permutations). As an alternative we provide a simple algorithm, Clever Look-ahead Volatility Reduction (CLVR). This algorithm constructs an ordering which approximately minimizes price volatility with a small computation cost. We also provide insight into the strategy changes that may occur if traders are subject to this sequencing algorithm.