Abstract
Today, many companies are using generative AI to streamline processes, summarize data, and assist with coding, among other things. They are exploring how companies can embed agents in processes (leading to changes in business models) and what it means to have digital colleagues, all based on LLMs—large language models that are trained on massive amounts of human-generated data. One of the interesting things companies can use LLMs for is to simulate how people (customers, competitors, partners) might behave in markets or strategic settings. The challenge, though, is that these simulations are inconsistent; sometimes they look strikingly human, and other times they fail badly. In this video presentation, Ben focuses on methods for making AI simulations more reliable, and on experimental evidence showing when and why those methods work.
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MIT CENTER FOR INFORMATION SYSTEMS RESEARCH (CISR)
Founded in 1974 and grounded in MIT's tradition of combining academic knowledge and practical purpose, MIT CISR helps executives meet the challenge of leading increasingly digital and data-driven organizations. We work directly with digital leaders, executives, and boards to develop our insights. Our research is funded by member organizations that support our work and participate in our consortium.
ANZ Banking Group (Australia)
Banco Bradesco S.A. (Brazil)
Commonwealth Superannuation Corp. (Australia)
Cuscal Limited (Australia)
DBS Bank Ltd. (Singapore)
Doosan Corporation (Korea)
Fomento Economico Mexicano, S.A.B., de C.V.
Mater Private Hospital (Ireland)
National Australia Bank Ltd.
Nomura Holdings, Inc. (Japan)
Nomura Research Institute, Ltd. Systems Consulting Division (Japan)
Novo Nordisk A/S (Denmark)
Pentagon Federal Credit Union
Principal Life Insurance Company
Reserve Bank of Australia
Scentre Group Limited (Australia)
Schneider Electric Industries SAS (France)
Tabcorp Holdings (Australia)
Telstra Limited (Australia)
Terumo Corporation (Japan)