[Theory] TODAY: 1/22 Talks at TTIC: Meena Jagadeesan, UC Berkeley

[Mary Marre via Theory]

*When:*        Wednesday, January 22, 2025 at* 11:30** am** CT   *


*Where:       *Talk will be given *live, in-person* at

                   TTIC, 6045 S. Kenwood Avenue

                   5th Floor, Room 530


*Virtually:*   *via panopto: **livestream*
<https://uchicago.hosted.panopto.com/Panopto/Pages/Viewer.aspx?id=f4a3b975-8eb2-4527-8bcd-b26601364ea7>


*                         Note: This has been restricted to TTIC/UChicago
Only*





*Who: *        Meena Jagadeesan, UC Berkeley



*Title*:         Steering Machine Learning Ecosystems of Interacting Agents

*Abstract*: Modern machine learning models—such as LLMs and recommender
systems—interact with humans, companies, and other models in a broader
ecosystem. However, these multi-agent interactions often induce unintended
ecosystem-level outcomes such as clickbait in classical content
recommendation ecosystems, and more recently, safety violations and market
concentration in nascent LLM ecosystems.

In this talk, I discuss my research on characterizing and steering
ecosystem-level outcomes. I take an economic and statistical perspective on
ML ecosystems, tracing outcomes back to the incentives of interacting
agents and to the ML pipeline for training models. First, in LLM
ecosystems, we show how analyzing a single model in isolation fails to
capture ecosystem-level performance trends: for example, training a model
with more resources can counterintuitively hurt ecosystem-level
performance. To help steer ecosystem-level outcomes, we develop technical
tools to assess how proposed policy interventions affect market entry,
safety compliance, and user welfare. Then, turning to content
recommendation ecosystems, we characterize a feedback loop between the
recommender system and content creators, which shapes the diversity and
quality of the content supply. Finally, I present a broader vision of ML
ecosystems where multi-agent interactions are steered towards the desired
algorithmic, market, and societal outcomes.

*Bio*: Meena Jagadeesan is a 5th year PhD student in Computer Science at
UC Berkeley, where she is advised by Michael I. Jordan and Jacob
Steinhardt. Her research investigates multi-agent interactions in machine
learning ecosystems from an economic and statistical perspective. She has
received an Open Philanthropy AI Fellowship and a Paul and Daisy Soros
Fellowship.

*Host: **Avrim Blum* <avrim at ttic.edu>





Mary C. Marre
Faculty Administrative Support
*Toyota Technological Institute*
*6045 S. Kenwood Avenue, Rm 517*
*Chicago, IL  60637*
*773-834-1757*
*mmarre at ttic.edu <mmarre at ttic.edu>*


On Tue, Jan 21, 2025 at 3:18 PM Mary Marre <mmarre at ttic.edu> wrote:

> *When:*        Wednesday, January 22, 2025 at* 11:30** am** CT   *
>
>
> *Where:       *Talk will be given *live, in-person* at
>
>                    TTIC, 6045 S. Kenwood Avenue
>
>                    5th Floor, Room 530
>
>
> *Virtually:*   *via panopto: **livestream*
> <https://uchicago.hosted.panopto.com/Panopto/Pages/Viewer.aspx?id=f4a3b975-8eb2-4527-8bcd-b26601364ea7>
>
>
> *                         Note: This has been restricted to TTIC/UChicago
> Only*
>
>
>
>
>
> *Who: *        Meena Jagadeesan, UC Berkeley
>
>
>
> *Title*:         Steering Machine Learning Ecosystems of Interacting
> Agents
>
> *Abstract*: Modern machine learning models—such as LLMs and recommender
> systems—interact with humans, companies, and other models in a broader
> ecosystem. However, these multi-agent interactions often induce unintended
> ecosystem-level outcomes such as clickbait in classical content
> recommendation ecosystems, and more recently, safety violations and market
> concentration in nascent LLM ecosystems.
>
> In this talk, I discuss my research on characterizing and steering
> ecosystem-level outcomes. I take an economic and statistical perspective on
> ML ecosystems, tracing outcomes back to the incentives of interacting
> agents and to the ML pipeline for training models. First, in LLM
> ecosystems, we show how analyzing a single model in isolation fails to
> capture ecosystem-level performance trends: for example, training a model
> with more resources can counterintuitively hurt ecosystem-level
> performance. To help steer ecosystem-level outcomes, we develop technical
> tools to assess how proposed policy interventions affect market entry,
> safety compliance, and user welfare. Then, turning to content
> recommendation ecosystems, we characterize a feedback loop between the
> recommender system and content creators, which shapes the diversity and
> quality of the content supply. Finally, I present a broader vision of ML
> ecosystems where multi-agent interactions are steered towards the desired
> algorithmic, market, and societal outcomes.
>
> *Bio*: Meena Jagadeesan is a 5th year PhD student in Computer Science at
> UC Berkeley, where she is advised by Michael I. Jordan and Jacob
> Steinhardt. Her research investigates multi-agent interactions in machine
> learning ecosystems from an economic and statistical perspective. She has
> received an Open Philanthropy AI Fellowship and a Paul and Daisy Soros
> Fellowship.
>
> *Host: **Avrim Blum* <avrim at ttic.edu>
>
>
>
>
> Mary C. Marre
> Faculty Administrative Support
> *Toyota Technological Institute*
> *6045 S. Kenwood Avenue, Rm 517*
> *Chicago, IL  60637*
> *773-834-1757*
> *mmarre at ttic.edu <mmarre at ttic.edu>*
>
>
> On Wed, Jan 15, 2025 at 4:51 PM Mary Marre <mmarre at ttic.edu> wrote:
>
>> *When:*        Wednesday, January 22, 2025 at* 11:30** am** CT   *
>>
>>
>> *Where:       *Talk will be given *live, in-person* at
>>
>>                    TTIC, 6045 S. Kenwood Avenue
>>
>>                    5th Floor, Room 530
>>
>>
>> *Virtually:*   *via panopto: **livestream*
>> <https://uchicago.hosted.panopto.com/Panopto/Pages/Viewer.aspx?id=f4a3b975-8eb2-4527-8bcd-b26601364ea7>
>>
>>
>> *                         Note: This has been restricted to TTIC/UChicago
>> Only*
>>
>>
>>
>>
>>
>> *Who: *        Meena Jagadeesan, UC Berkeley
>>
>>
>>
>> *Title*:         Steering Machine Learning Ecosystems of Interacting
>> Agents
>>
>> *Abstract*: Modern machine learning models—such as LLMs and recommender
>> systems—interact with humans, companies, and other models in a broader
>> ecosystem. However, these multi-agent interactions often induce unintended
>> ecosystem-level outcomes such as clickbait in classical content
>> recommendation ecosystems, and more recently, safety violations and market
>> concentration in nascent LLM ecosystems.
>>
>> In this talk, I discuss my research on characterizing and steering
>> ecosystem-level outcomes. I take an economic and statistical perspective on
>> ML ecosystems, tracing outcomes back to the incentives of interacting
>> agents and to the ML pipeline for training models. First, in LLM
>> ecosystems, we show how analyzing a single model in isolation fails to
>> capture ecosystem-level performance trends: for example, training a model
>> with more resources can counterintuitively hurt ecosystem-level
>> performance. To help steer ecosystem-level outcomes, we develop technical
>> tools to assess how proposed policy interventions affect market entry,
>> safety compliance, and user welfare. Then, turning to content
>> recommendation ecosystems, we characterize a feedback loop between the
>> recommender system and content creators, which shapes the diversity and
>> quality of the content supply. Finally, I present a broader vision of ML
>> ecosystems where multi-agent interactions are steered towards the desired
>> algorithmic, market, and societal outcomes.
>>
>> *Bio*: Meena Jagadeesan is a 5th year PhD student in Computer Science at
>> UC Berkeley, where she is advised by Michael I. Jordan and Jacob
>> Steinhardt. Her research investigates multi-agent interactions in machine
>> learning ecosystems from an economic and statistical perspective. She has
>> received an Open Philanthropy AI Fellowship and a Paul and Daisy Soros
>> Fellowship.
>>
>> *Host: **Avrim Blum* <avrim at ttic.edu>
>>
>>
>>
>>
>> Mary C. Marre
>> Faculty Administrative Support
>> *Toyota Technological Institute*
>> *6045 S. Kenwood Avenue, Rm 517*
>> *Chicago, IL  60637*
>> *773-834-1757*
>> *mmarre at ttic.edu <mmarre at ttic.edu>*
>>
>
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