Held at Google, Mountain View, CA
Organized by Graham Spencer, Trustee, Santa Fe Institute and Engineering Director, Google, David Krakauer, Chair of Faculty and Professor, Santa Fe Institute, and Chris Wood, Vice President of Administration and Director of Business Network, Santa Fe Institute.
Organized by SFI's Business Network and Education Offices
Held at the Santa Fe Institute
February 9-11, 2011, Santa Fe, New Mexico
Co-Organized by the Santa Fe Business Network and Humana Inc.
Healthcare Economic Forum
SFI Business Network Topical Meeting
Wednesday February 9
Humana Healthcare Simulator
Thursday and Friday February 10-11
The ever-increasing size and complexity of large-scale software systems is one of the greatest challenges facing business, government, and academia today. Such systems are "interdependent webs of software-intensive systems, people, policies, cultures, and economics", representing unprecedented aggregations of one or more of the following quantities": (1) lines of code; (2) amount of data stored, accessed, manipulated, and refined; (3) number of connections and interdependencies; (4) number of hardware elements; (5) number of computational elements; (6) number of system purposes and user perception of these purposes; (7) number of routine processes, interactions, and “emergent behaviors”; (8) number of (overlapping) policy domains and enforceable mechanisms; (9) number of people involved in some way ((DOD Study on Ultra-Large-Scale Software Systems).
This SFI Business Network Topical Meeting, jointly organized by Fidelity and SFI, will explore ways in which the study of complex systems in other domains of science and technology can be valuable for large-scale software systems, survey the state of the art in verification and validation of large-scale software systems from the academic and corporate perspectives, and consider implications of large-scale systems for the financial industry.
August 24, 2011
Held at National Semiconductor Corporation, 2900 Semiconductor Dr., Santa Clara, CA
Organized by Chander Chawla and David Hanson, National Semiconductor Corporation and Chris Wood, Santa Fe Institute
September 21, 2011
Held at the Hilton Santa Fe, 100 Sandoval Street, Santa Fe, NM
Organized by Rick Stephens and Michael Richey, Boeing Inc., Nora Sabelli of SRI, and Ginger Richardson and Chris Wood, Santa Fe Institute
October 5, 2011
Held at Morgan Stanley World Headquarters, 1585 Broadway, 41st Floor, New York, NY
Organized by Michael Mauboussin, Legg Mason Capital Management and SFI, John Rundle, UC Davis and SFI, and Chris Wood, SFI
SFI / NMC Program on Combining Information Theory and Game Theory
Organizers: David Wolpert (SFI External Faculty), Simon DeDeo (SFI Omidyar Fellow), Nils Bertschinger (Max Planck), Eckehard Olbrich (Max Planck), Eric Smith (SFI External Faculty), Luis Bettencourt (SFI)
How a single agent (human, firm, animal, etc.) behaves typically depends on what information it has about its environment, and on its preferences. Accordingly, the joint behavior of multiple interacting agents can depend strongly on the information available to the separate agents, both about one another, and about external random variables. Precisely how the joint behavior depends on the information available to the agents is determined by the preferences of those agents. So in general there is a strong interplay among the preferences of all the agents, their behavior, and the information structure connecting them.
One tool that might help us understand this interplay is Shannon information theory. In Shannon information theory, information is a function of a distribution. Increasing the amount of information in a distribution means making that distribution more tightly concentrated. This definition applies not only if the support of the distribution shrinks or expands, but also if it moves.
Another tool that might help us understand the interplay is game theory. In contrast to Shannon information theory, game theory does not quantify information in terms of properties of probability distributions. Rather the information available to a player is quantified as an "information set," specifying a set of states the world might be in. The amount of information available to a player increases if such an information set shrinks. In contrast to the case with Shannon information theory, the change in information for moving an information set is undefined.
There are other differences between information theory and game theory. For example, whereas the foundations of Shannon information theory concern a single player (the designer of a communication network), the foundations of game theory concern multiple players.
Reconciling the different perspectives on information in Shannon information theory and game theory could have many benefits. Most directly, it may help us understand the interplay among the preferences of a set of interacting players, their behavior, and the information structure connecting them. As potential examples, it might help us address issues like the following:
1. How do information theoretic quantifications of the joint behavior of a set of interacting players (e.g., mutual information between actions of pairs of them) vary with changes to the preferences of those players?
2. Can relating the philosophical foundations of the two fields improve them? For example, as Shannon himself emphasized, Shannon information is purely "syntactic," quantifying the amount of information in a distribution purely by how concentrated it is. Can the utility functions of game theory—which depend not just on how concentrated a distribution is, but also on where it is concentrated—be used to define a "semantic" variant of Shannon information?
3. Can relating the mathematical formalisms of the two fields improve them? For example, are there analogs of the powerful theorems of information theory for game-theoretic quantities, e.g., game theoretic versions of results concerning rate distortion tradeoffs, the data processing inequality, etc.?
More generally, greater understanding of the relation between information theory and game theory may generate breakthroughs in many disciplines, including economics, political science, cognitive sciences and artificial intelligence.
Full web page here.
"Innovation" is simultaneously: (a) one of the most over-used and increasingly meaningless buzz-words in the business lexicon; and (b) one of the most important but least understood human capabilities upon which business success depends. This informal event will explore innovation in all its breadth, from the perspective of innovation in evolutionary biology (which is perhaps the most compelling example of innovation in the universe) to the perspective of innovation in human-created technology (which is one form of innovation of significant interest in the corporate world).
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