Three Minimal Market Institutions with Human and Algorithmic Agents: Theory and Experimental Evidence

We define and examine three minimal market games (sell-all, buy-sell, and double auction) in the laboratory relative to the predictions of theory. These closed exchange economies have some cash to facilitate transactions, and include feedback. The experiment reveals that (1) the competitive general equilibrium (CGE) and non-cooperative (NCE) models are reasonable anchors to locate most but not all the observed outcomes of the three market mechanisms; (2) outcomes tend to get closer to CGE predictions as the number of players increases; (3) prices and allocations in double auctions deviate persistently from CGE predictions; (4) the outcome paths across the three market mechanisms differ significantly and persistently; (5) importance of market structures for outcomes is reinforced by algorithmic trader simulations; and (6) none of the three markets dominates the others across six measures of performance. Inclusion of some mechanism differences into theory may enhance our understanding of important aspects of markets.Strategic market games, Laboratory experiments, Minimally intelligent agents, Adaptive learning agents, General equilibrium

Deep Learning can Replicate Adaptive Traders in a Limit-Order-Book Financial Market

We report successful results from using deep learning neural networks (DLNNs) to learn, purely by observation, the behavior of profitable traders in an electronic market closely modelled on the limit-order-book (LOB) market mechanisms that are commonly found in the real-world global financial markets for equities (stocks & shares), currencies, bonds, commodities, and derivatives. Successful real human traders, and advanced automated algorithmic trading systems, learn from experience and adapt over time as market conditions change; our DLNN learns to copy this adaptive trading behavior. A novel aspect of our work is that we do not involve the conventional approach of attempting to predict time-series of prices of tradeable securities. Instead, we collect large volumes of training data by observing only the quotes issued by a successful sales-trader in the market, details of the orders that trader is executing, and the data available on the LOB (as would usually be provided by a centralized exchange) over the period that the trader is active. In this paper we demonstrate that suitably configured DLNNs can learn to replicate the trading behavior of a successful adaptive automated trader, an algorithmic system previously demonstrated to outperform human traders. We also demonstrate that DLNNs can learn to perform better (i.e., more profitably) than the trader that provided the training data. We believe that this is the first ever demonstration that DLNNs can successfully replicate a human-like, or super-human, adaptive trader operating in a realistic emulation of a real-world financial market. Our results can be considered as proof-of-concept that a DLNN could, in principle, observe the actions of a human trader in a real financial market and over time learn to trade equally as well as that human trader, and possibly better.Comment: 8 pages, 4 figures. To be presented at IEEE Symposium on Computational Intelligence in Financial Engineering (CIFEr), Bengaluru; Nov 18-21, 201

Cryptocurrency with a Conscience: Using Artificial Intelligence to Develop Money that Advances Human Ethical Values

Cryptocurrencies like Bitcoin are offering new avenues for economic empowerment to individuals around the world. However, they also provide a powerful tool that facilitates criminal activities such as human trafficking and illegal weapons sales that cause great harm to individuals and communities. Cryptocurrency advocates have argued that the ethical dimensions of cryptocurrency are not qualitatively new, insofar as money has always been understood as a passive instrument that lacks ethical values and can be used for good or ill purposes. In this paper, we challenge such a presumption that money must be ‘value-neutral.’ Building on advances in artificial intelligence, cryptography, and machine ethics, we argue that it is possible to design artificially intelligent cryptocurrencies that are not ethically neutral but which autonomously regulate their own use in a way that reflects the ethical values of particular human beings – or even entire human societies. We propose a technological framework for such cryptocurrencies and then analyse the legal, ethical, and economic implications of their use. Finally, we suggest that the development of cryptocurrencies possessing ethical as well as monetary value can provide human beings with a new economic means of positively influencing the ethos and values of their societies

Aggregation of Diverse Information with Double Auction Trading among Minimally-Intelligent Algorithmic Agents

Information dissemination and aggregation are key economic functions of financial markets. How intelligent do traders have to be for the complex task of aggregating diverse information (i.e., approximate the predictions of the rational expectations equilibrium) in a competitive double auction market? An apparent ex-ante answer is: intelligent enough to perform the bootstrap operation necessary for the task—to somehow arrive at prices that are needed to generate those very prices. Constructing a path to such equilibrium through rational behavior has remained beyond what we know of human cognitive abilities. Yet, laboratory experiments report that profit motivated human traders are able to aggregate information in some, but not all, market environments (Plott and Sunder 1988, Forsythe and Lundholm 1990). Algorithmic agents have the potential to yield insights into how simple individual behavior may perform this complex market function as an emergent phenomenon. We report on a computational experiment with markets populated by algorithmic traders who follow cognitively simple heuristics humans are known to use. These markets, too, converge to rational expectations equilibria in environments in which human markets converge, albeit slowly and noisily. The results suggest that high level of individual intelligence or rationality is not necessary for efficient outcomes to emerge at the market level; the structure of the market itself is a source of rationality observed in the outcomes

A Grey-Box Approach to Automated Mechanism Design

Auctions play an important role in electronic commerce, and have been used to solve problems in distributed computing. Automated approaches to designing effective auction mechanisms are helpful in reducing the burden of traditional game theoretic, analytic approaches and in searching through the large space of possible auction mechanisms. This paper presents an approach to automated mechanism design (AMD) in the domain of double auctions. We describe a novel parametrized space of double auctions, and then introduce an evolutionary search method that searches this space of parameters. The approach evaluates auction mechanisms using the framework of the TAC Market Design Game and relates the performance of the markets in that game to their constituent parts using reinforcement learning. Experiments show that the strongest mechanisms we found using this approach not only win the Market Design Game against known, strong opponents, but also exhibit desirable economic properties when they run in isolation.Comment: 18 pages, 2 figures, 2 tables, and 1 algorithm. Extended abstract to appear in the proceedings of AAMAS'201

Simple Agents, Intelligent Markets

Attainment of rational expectations equilibria in asset markets calls for the price system to disseminate agents’ private information to others. Markets populated by human agents are known to be capable of converging to rational expectations equilibria. This paper reports comparable market outcomes when human agents are replaced by boundedly-rational algorithmic agents who use a simple means-end heuristic. These algorithmic agents lack the capability to optimize; yet outcomes of markets populated by them converge near the equilibrium derived from optimization assumptions. These findings point to market structure (rather than cognition or optimization) being an important determinant of efficient aggregate level outcomes

Generative sound art as poeitic poetry for an information society

This paper considers computer music in relation to broader society and asks what algorithmic composition can learn from the metaphysical shift which is happening in the so-called information societies. This is explored by taking the mapping problem inherent in the use of extra- musical models in generative composition and presenting a simple generative schema which prioritises sound, ex- ploiting the generative potential of digital audio. It is sug- gested that the exploration of such models has more than aesthetic relevance and that the interdisciplinary nature of digital sound art represents a microcosm of an emerging reality, thereby constituting a poietic playground for com- ing to terms with the implications and challenges of the information age