Study of Artificial Financial Markets with Adaptive Trading Agents

Artificial Intelligence Laboratory and The Center for Biological and Computational Learning Massachusetts Institute of Technology Cambridge, Massachusetts

In Artificial Market Dynamics, we have constructed various simulations to study (a) the efficiency of information aggregation and dissemination in a market, and (b) performance and characteristics of automated market-making strategies. In the study of information aggregation and dissemination, we have tried trading agents with identical, and heterogenous preferences[1]. The result shows that our trading agents can accurately infer and aggregate diverse pieces of information in many circumstances, and they have difficulties in cases where human traders are also unable to determine the rational expectations of equilibrium.

For Theoretical and Computational Studies of Market Equilibrium, we will use artificial markets with software agents to simulate such market equilibrium. There are many trading strategies that we can design and will study how the market equilibrium emerges and how this is related to individual’s preferences.

For the Web Market, its main goal is to provide a test bed for conducting large scale market experiments involving both human and artificial trader and market makers. The Web Market is an Internet-based electronic market which is designed to be fully automated in the trading process.

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University Of Oxford

Artificial intelligence

don’t be evil and make things better

I am one of the signatories of an open letter calling for a stronger aim at socially beneficial artificial intelligence. It might seem odd to call for something like that: who in their right mind would not want AI to be beneficial? But when we look at the field (and indeed, many other research fields) the focus has traditionally been on making AI more capable. Besides some pure research interest and no doubt some “let’s create life”-ambition, the bulk of motivation has been to make systems that do something useful (or push in the direction of something useful). “Useful” is normally defined in term of performing some task – translation, driving, giving medical advice – rather than having a good impact on the world. Better done tasks are typically good locally – people get translations more cheaply, things get transported, advice may be better – but have more complex knock-on effects: fewer translators, drivers or doctors needed, or that their jobs get transformed, plus potential risks from easy (but possibly faulty) translation, accidents and misuse of autonomous vehicles, or changes in liability. Way messier. Even if the overall impact is great, socially disruptive technologies that appear surprisingly fast can cause trouble, emergent misbehaviour and bad design choices can lead to devices that amplify risk (consider high frequency trading, badly used risk models, or anything that empowers crazy people). Some technologies may also lend themselves to centralising power (surveillance, autonomous weapons) but reduce accountability (learning algorithms internalising discriminatory assumptions in an opaque way). These considerations should of course be part of any responsible engineering and deployment, even if handling them is by no means solely the job of the scientist or programmer. Doing it right will require far more help from other disciplines..

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University Of Pennsylvania


Way up in a New York skyscraper, inside the headquarters of Lehman Brothers Holdings Inc., Michael Kearns is trying to teach a computer to do something other machines can’t: think like a Wall Street trader. In his cubicle overlooking the trading f loor, Kearns, 44, consults with Lehman Brothers traders as Ph.D.s tap away at secret software. The programs they’re writing are designed to sift through billions of trades and spot subtle patterns in world markets. Kearns, a computer scientist who has a doctorate from Harvard University, says the code is part of a dream he’s been chasing for more .

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