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Difference between revisions of "Machine Learning, Complexity and Market Behavior"

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New York, New York
 
New York, New York
 
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What impact will the increasing use of artificial intelligence (AI), machine learning (ML), and other computational tools have on financial markets? Specifically, how might these tools shape market behavior, and even the nature of the markets themselves? We are still crafting this meeting’s agenda, but existing work by a number of SFI community members provide useful insights on these questions. For example Andrew Lo’s recent paper on Moore’s vs. Murphy’s Laws offers a broad overview of the complexities governing the impact of new financial technologies on market behavior. <br/><br/>Most experts agree that the increased use of these computational tools will increase heterogeneity, and computational models are particularly useful. This paper by Blake LeBaron on the application of heterogeneity of learning to modeling asset price dynamics is a good example of this approach. Less well understood, is the how the use of these tools will impact the collective intelligence of financial institutions and the collective computation of the markets they animate. This overview from The Atlantic provides a good summary of these emerging fields, and this paper by Eleanor Brush, David Krakauer, and Jessica Flack offers a useful two stage model for thinking about collective computation.
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What impact will the increasing use of artificial intelligence (AI), machine learning (ML), and other computational tools have on financial markets? Specifically, how might these tools shape market behavior, and even the nature of the markets themselves? We are still crafting this meeting’s agenda, but existing work by a number of SFI community members provide useful insights on these questions. For example [https://www.santafe.edu/people/profile/andrew-lo Andrew Lo’s] recent [http://alo.mit.edu/wp-content/uploads/2017/06/Moores-Law-Vs.-Murphys-Law-in-the-Financial-System-Whos-Winning.pdf paper] on Moore’s vs. Murphy’s Laws offers a broad overview of the complexities governing the impact of new financial technologies on market behavior. <br/><br/>Most experts agree that the increased use of these computational tools will increase heterogeneity, and computational models are particularly useful. [https://www.sciencedirect.com/science/article/pii/S0167268112000546 This paper] by [http://www.brandeis.edu/facultyguide/person.html?emplid=3863836c60fea8a993359c6d2f71be423bc77a23 Blake LeBaron] on the application of heterogeneity of learning to modeling asset price dynamics is a good example of this approach. Less well understood, is the how the use of these tools will impact the collective intelligence of financial institutions and the collective computation of the markets they animate. [https://www.theatlantic.com/science/archive/2017/07/collective-computation/533169/?utm_source=atltw This overview] from The Atlantic provides a good summary of these emerging fields, and [http://advances.sciencemag.org/content/4/1/e1603311 this paper] by [http://www.terpconnect.umd.edu/~ebrush/ Eleanor Brush], [https://www.santafe.edu/people/profile/david-krakauer David Krakauer], and [https://www.santafe.edu/people/profile/jessica-flack Jessica Flack] offers a useful two stage model for thinking about collective computation.
 
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Revision as of 17:48, 21 June 2019

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SFI ACtioN Topical Meeting


August 8, 2019
New York, New York


What impact will the increasing use of artificial intelligence (AI), machine learning (ML), and other computational tools have on financial markets? Specifically, how might these tools shape market behavior, and even the nature of the markets themselves? We are still crafting this meeting’s agenda, but existing work by a number of SFI community members provide useful insights on these questions. For example Andrew Lo’s recent paper on Moore’s vs. Murphy’s Laws offers a broad overview of the complexities governing the impact of new financial technologies on market behavior.

Most experts agree that the increased use of these computational tools will increase heterogeneity, and computational models are particularly useful. This paper by Blake LeBaron on the application of heterogeneity of learning to modeling asset price dynamics is a good example of this approach. Less well understood, is the how the use of these tools will impact the collective intelligence of financial institutions and the collective computation of the markets they animate. This overview from The Atlantic provides a good summary of these emerging fields, and this paper by Eleanor Brush, David Krakauer, and Jessica Flack offers a useful two stage model for thinking about collective computation.