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'''August 8, 2019'''<br />
 
'''August 8, 2019'''<br />
New York, New York
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[https://www.google.com/maps/place/UBS+Financial+Services+Inc./@40.7609426,-73.9800239,15z/data=!4m2!3m1!1s0x0:0xa47f2028bf65fd5a?sa=X&ved=2ahUKEwjik5bTwMnjAhX5KDQIHe34DnQQ_BIwCnoECA8QCA UBS]<br/>
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1285 6th Avenue (Enter on 51st Street)<br/>
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New York, New York 10019
 
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'''Morning Sessions - Collective Intelligence'''<br/>
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In a meeting co-organized by the Santa Fe Institute (SFI) and UBS, scientists and practitioners will explore the possible impacts of the increasing use of artificial intelligence (AI), machine learning (ML), and other computational tools on financial markets. Specifically, how might these tools shape market behavior, and even the nature of the markets themselves? The day will be divided into two sessions.<br/><br/>
The new field of collective intelligence (see ''The Atlantic’s'' [https://www.theatlantic.com/science/archive/2017/07/collective-computation/533169/?utm_source=atltw overview here]) provides a useful lens for considering the ways increased machine learning tools might change market behavior. The first talk, given by Professor [https://www.santafe.edu/people/profile/jessica-flack Jessica Flack], will provide an overview of the collective intelligence phenomena. This 30 minute talk will be followed by a 30 minute group discussion. An important goal for the talk and the discussion is to illuminate why the market is itself a collective intelligence. The second talk, given by Professor [https://www.santafe.edu/people/profile/david-krakauer David Krakauer], will explore how machine learning tools have affected non-market collective intelligences. A key goal of this talk, and the subsequent discussion, is to look for patterns in the impact of machine learning tools on collective intelligences across three domains:<br/>
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'''Morning Sessions - Collective Intelligence and Collective Computation'''<br/>
# Interfaces and cognitive bottlenecks<br/>
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The new field of collective intelligence (see ''The Atlantic’s'' [https://www.theatlantic.com/science/archive/2017/07/collective-computation/533169/?utm_source=atltw overview here]) provides a useful lens for considering the ways increased machine learning tools might change market behavior. The first talk, given by SFI Professor [https://www.santafe.edu/people/profile/jessica-flack Jessica Flack], will provide an overview of the collective intelligence phenomena. This 25 minute talk will be followed by a 25 minute group discussion. An important goal for the talk and the discussion is to illuminate why the market is itself a collective intelligence. The second talk, given by SFI Professor and President [https://www.santafe.edu/people/profile/david-krakauer David Krakauer], will explore how machine learning tools have affected non-market collective intelligences, looking for patterns in the impact of machine learning tools on collective intelligences across three domains: interface and cognitive bottlenecks; authority and homogeneity; strategic, or game theoretic, behavior.<br/><br/>
# Authority and homogeneity<br/>
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Following the morning talks and discussions, there will be a 70-minute panel of finance industry experts. The panel will begin with each participant offering their own insights (approx. 10 min each) as to how they believe machine learning tools are changing the behavior of markets. Subsequent moderated discussion with the panelists (approx. 15 min) and with the panelists and the whole room (approx. 15 min) will more deeply explore how the collective intelligence lens can help us understand the ways machine learning is changing market behavior.<br/><br/>
# Strategic, or game theoretic, behavior<br/><br/>
 
Following the morning talks and discussions, there will be a 90-minute practitioner panel. The panel will begin with each participant offering their own insights (approx. 10 min each) as to how they believe machine learning tools are changing the behavior of markets. Subsequent moderated discussion with the panelists (approx. 25 min) and with the panelists and the whole room (approx. 25 min) will more deeply explore how the collective intelligence lens can help us understand the ways machine learning is changing market behavior.<br/><br/>
 
 
'''Afternoon Sessions – Machine Learning in Markets and Increased Homogeneity'''<br/>
 
'''Afternoon Sessions – Machine Learning in Markets and Increased Homogeneity'''<br/>
We have already observed a host of mechanisms through which machine learning and other new technologies have affected financial markets (see [https://alo.mit.edu/wp-content/uploads/2017/06/Moores-Law-Vs.-Murphys-Law-in-the-Financial-System-Whos-Winning.pdf overview here]). The first talk, given by Professor [https://www.santafe.edu/people/profile/andrew-lo Andrew Lo], will provide an overview as to how these tools are changing market behavior. One notable mechanism involves increased homogeneity of market strategies. (As noted above, this is one of the canonical ways machine learning can affect collective intelligences.) In the second afternoon lecture, Professor [http://www.brandeis.edu/facultyguide/person.html?emplid=3863836c60fea8a993359c6d2f71be423bc77a23 Blake LeBaron] will use computational models to more fully explore the relationship between decreased strategic variety and market behavior. Following the afternoon talks and discussions, there will be a 90-minute practitioner panel. The panel will begin with each participant offering their own insights (approx. 10 min each) as to how they believe machine learning tools are affecting the behavior of markets. Subsequent moderated discussion with the panelists (approx. 25 min) and with the panelists and the whole room (approx. 25 min) will more deeply explore strategic homogeneity and other mechanism by which machine learning impacts market behavior.<br/>
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This session will concentrate on the use of technology in market decision-making, and the related increase in the homogeneity of market strategy. We have already observed a host of mechanisms through which machine learning and other new technologies have affected financial markets (see [https://alo.mit.edu/wp-content/uploads/2017/06/Moores-Law-Vs.-Murphys-Law-in-the-Financial-System-Whos-Winning.pdf overview here]). The first talk, given by Columbia University Professor [https://moallemi.com/ciamac/ Ciamac Moallemi] will explore machine learning's applications to financial strategy. In the second afternoon lecture, Professor [http://www.brandeis.edu/facultyguide/person.html?emplid=3863836c60fea8a993359c6d2f71be423bc77a23 Blake LeBaron] of Brandeis University will use computational models to more fully explore the relationship between decreased strategic variety and market behavior. Finally, MIT Professor and SFI External Faculty Member [https://www.santafe.edu/people/profile/andrew-lo Andrew Lo], will provide an overview of how the effects of machine learning are felt in the market.
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<br/><br/>Following the afternoon talks and discussions, there will be a final 70-minute panel of finance industry experts. The panel will begin with each participant offering their own insights (approx. 10 min each) as to how they believe machine learning tools are affecting the behavior of markets. Subsequent moderated discussion with the panelists (approx. 15 min) and with the panelists and the whole room (approx. 15 min) will more deeply explore strategic homogeneity and other mechanism by which machine learning impacts market behavior.<br/>

Latest revision as of 19:05, 31 July 2019

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Asimov Robot.jpg

SFI ACtioN Topical Meeting


August 8, 2019
UBS
1285 6th Avenue (Enter on 51st Street)
New York, New York 10019


In a meeting co-organized by the Santa Fe Institute (SFI) and UBS, scientists and practitioners will explore the possible impacts of the increasing use of artificial intelligence (AI), machine learning (ML), and other computational tools on financial markets. Specifically, how might these tools shape market behavior, and even the nature of the markets themselves? The day will be divided into two sessions.

Morning Sessions - Collective Intelligence and Collective Computation
The new field of collective intelligence (see The Atlantic’s overview here) provides a useful lens for considering the ways increased machine learning tools might change market behavior. The first talk, given by SFI Professor Jessica Flack, will provide an overview of the collective intelligence phenomena. This 25 minute talk will be followed by a 25 minute group discussion. An important goal for the talk and the discussion is to illuminate why the market is itself a collective intelligence. The second talk, given by SFI Professor and President David Krakauer, will explore how machine learning tools have affected non-market collective intelligences, looking for patterns in the impact of machine learning tools on collective intelligences across three domains: interface and cognitive bottlenecks; authority and homogeneity; strategic, or game theoretic, behavior.

Following the morning talks and discussions, there will be a 70-minute panel of finance industry experts. The panel will begin with each participant offering their own insights (approx. 10 min each) as to how they believe machine learning tools are changing the behavior of markets. Subsequent moderated discussion with the panelists (approx. 15 min) and with the panelists and the whole room (approx. 15 min) will more deeply explore how the collective intelligence lens can help us understand the ways machine learning is changing market behavior.

Afternoon Sessions – Machine Learning in Markets and Increased Homogeneity
This session will concentrate on the use of technology in market decision-making, and the related increase in the homogeneity of market strategy. We have already observed a host of mechanisms through which machine learning and other new technologies have affected financial markets (see overview here). The first talk, given by Columbia University Professor Ciamac Moallemi will explore machine learning's applications to financial strategy. In the second afternoon lecture, Professor Blake LeBaron of Brandeis University will use computational models to more fully explore the relationship between decreased strategic variety and market behavior. Finally, MIT Professor and SFI External Faculty Member Andrew Lo, will provide an overview of how the effects of machine learning are felt in the market.

Following the afternoon talks and discussions, there will be a final 70-minute panel of finance industry experts. The panel will begin with each participant offering their own insights (approx. 10 min each) as to how they believe machine learning tools are affecting the behavior of markets. Subsequent moderated discussion with the panelists (approx. 15 min) and with the panelists and the whole room (approx. 15 min) will more deeply explore strategic homogeneity and other mechanism by which machine learning impacts market behavior.