My intended PhD dissertation is on collaborative decision-making. This topic came to my attention after studying collaborative search and conflict mediation when working on CREST, a collaborative real-estate search tool designed to support a group of users to search for and agree on a property asynchronously, and studying a variety of misinformation-related papers when working on the Datavoids project. Both projects shed light on the fact that any collaboration endeavor is rendered useless if no decision is desired by the group. In CREST, the team needs to bear in mind that the final objective of booking a single property supersedes any and all collaboration issues that arise. In the misinformation/data voids projects, a group of misinformation mitigators, usually in the form of a dedicated trust and safety team, need to collaborate to reach the final goal of freeing the masses from dangerous disinformation operations. A very exciting computational angle to this problem is uncertainty: how do collaborative decisions fare in highly uncertain environments? Does collaboration reduce the complexity brought forth by uncertainty through reduced cognitive overload and knowledge-sharing by the group?
As a way to keep myself motivated and accountable for the next few years, I decided to maintain this document where I will expansively collect resources that will inspire an ever-growing pipeline of projects I can work on. Following the advice of my wonderful advisor, I am practicing a "writing-first" approach where I materialize the convoluted, yet somewhat sensical, mess in my head and bring it from conception to fruition in what I hope are innovative and useful projects for the research community.
Below are some keywords/topics I have been using to gather resources.
Keywords: collaboration, groupware, crowd-work, distributed collaboration, asynchronous collaboration.
Topics to learn: constrained optimization problems
- http://sites.computer.org/debull/A22sept/p102.pdf
- https://wp.sigmod.org/?p=3504
- https://en.wikipedia.org/wiki/Constrained_optimization
Reinforcement learning agent for collaborative decision-making scenarios? - Datavoids misinformation project
Interesting economic professor that studies collaborative decision-making at NYUAD: Andrzej Baransk
- http://nebula.wsimg.com/9281e1e0b6c530382670f79d2db8ebdc?AccessKeyId=D0C575140B2894DE68E3&disposition=0&alloworigin=1
- https://nyuad.nyu.edu/en/academics/divisions/social-science/faculty/andrzej-baranski.html
The value of isolation and deepwork for group decision making, inspired by what I read in Cal Newport's Deepwork
- Fairness?
Cooperative Game Theory: https://en.wikipedia.org/wiki/Cooperative_game_theory
https://www.selcukozyurt.com/advancedgametheory
Professor ozyurt also has very cool primers on math and game theory for all levels on his youtube channel: https://www.youtube.com/@selcukozyurt
game theory primer: https://www.youtube.com/playlist?list=PL76B0EB6DDFC42D02
The role of collaboration in enabling people with disabilities to being more productive or to enhance learning: https://dl.acm.org/doi/10.1145/3544548.3581261
The use of more advanced technological modalities for collaborative decision-making
More random papers:
Game theory resources:
- https://www.youtube.com/@Gametheory101/playlists
- https://www.gametheory.online/
- roughgarden Computer science and economics have had a productive relationship over the past 25 years, resulting in a new field called algorithmic game theory or alternatively economics and computation. Many problems central to modern computer science, ranging from resource allocation in large networks to the design of blockchain protocols, fundamentally involve interactions between multiple self-interested parties. Economics and game theory offer a host of useful models and definitions to reason about such problems. The flow of ideas also travels in the other direction, with approximation and complexity notions playing an increasingly important role in the latest developments in economic theory. My textbook and accompanying lecture videos offer an accessible introduction to the subject, with case studies on online advertising, wireless spectrum auctions, kidney exchange, and network management.
- Twenty Lectures on Algorithmic Game Theory, Cambridge University Press, 2016.
- 20 Video Lectures on Algorithmic Game Theory (from Stanford's CS364A, Fall 2013). For a "reader's digest" version, see
- Algorithmic Game Theory, Communications of the ACM, July 2010. (Preprint) For two different one-hour general-audience talks, see
- Equilibria, Computation, and Compromises (video), Lens of Computation on the Sciences, Institute for Advanced Studies (2014). Slides
- How Computer Science Informs Modern Auction Design (video), Simons Foundation Public Lecture (2018). Slides and for a short course suitable for first-year undergraduates see
- Incentives in Computer Science playlist (derived from Stanford's CS269I). The following collection is older and targeted more to researchers than to a general audience, but is still useful for several topics.
- (co-edited with Noam Nisan, Eva Tardos, and Vijay Vazirani) Algorithmic Game Theory, Cambridge University Press, 2007. Read the entire book online by clicking here (look under the "Resources" tab). Computer science and economics have had a productive relationship over the past 25 years, resulting in a new field called algorithmic game theory or alternatively economics and computation. Many problems central to modern computer science, ranging from resource allocation in large networks to the design of blockchain protocols, fundamentally involve interactions between multiple self-interested parties. Economics and game theory offer a host of useful models and definitions to reason about such problems. The flow of ideas also travels in the other direction, with approximation and complexity notions playing an increasingly important role in the latest developments in economic theory. My textbook and accompanying lecture videos offer an accessible introduction to the subject, with case studies on online advertising, wireless spectrum auctions, kidney exchange, and network management.
- Twenty Lectures on Algorithmic Game Theory, Cambridge University Press, 2016.
- 20 Video Lectures on Algorithmic Game Theory (from Stanford's CS364A, Fall 2013). For a "reader's digest" version, see
- Algorithmic Game Theory, Communications of the ACM, July 2010. (Preprint) For two different one-hour general-audience talks, see
- Equilibria, Computation, and Compromises (video), Lens of Computation on the Sciences, Institute for Advanced Studies (2014). Slides
- How Computer Science Informs Modern Auction Design (video), Simons Foundation Public Lecture (2018). Slides and for a short course suitable for first-year undergraduates see
- Incentives in Computer Science playlist (derived from Stanford's CS269I). The following collection is older and targeted more to researchers than to a general audience, but is still useful for several topics.
- (co-edited with Noam Nisan, Eva Tardos, and Vijay Vazirani) Algorithmic Game Theory, Cambridge University Press, 2007. Read the entire book online by clicking here (look under the "Resources" tab).
Group Think and cognitive tunneling! a new research avenue inspired by the readings of my group dynamics course.
[[ ]] Daniel Kahneman , author of thinking fast and slow, and Amos Tversky on decision making from a psychological perspective
adverserial collaboration: https://www.youtube.com/watch?v=sW5sMgGo7dw
Decision making under uncertainty at stanford: https://aa228.stanford.edu/
franco talks about his creatve process, maybe we can create tools that support artists in their creative endeavor: https://www.youtube.com/watch?v=SIWd0GX6u-E