Data sharing games
- URL: http://arxiv.org/abs/2101.10721v1
- Date: Tue, 26 Jan 2021 11:29:01 GMT
- Title: Data sharing games
- Authors: V\'ictor Gallego, Roi Naveiro, David R\'ios Insua, Wolfram Rozas
- Abstract summary: Data sharing issues pervade online social and economic environments.
We formalize this interaction as a game, the data sharing game, based on the Iterated Prisoner's Dilemma.
We consider several strategies for how the citizens may behave, depending on the degree of centralization sought.
- Score: 1.3764085113103222
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Data sharing issues pervade online social and economic environments. To
foster social progress, it is important to develop models of the interaction
between data producers and consumers that can promote the rise of cooperation
between the involved parties. We formalize this interaction as a game, the data
sharing game, based on the Iterated Prisoner's Dilemma and deal with it through
multi-agent reinforcement learning techniques. We consider several strategies
for how the citizens may behave, depending on the degree of centralization
sought. Simulations suggest mechanisms for cooperation to take place and, thus,
achieve maximum social utility: data consumers should perform some kind of
opponent modeling, or a regulator should transfer utility between both players
and incentivise them.
Related papers
- The Dimensions of Data Labor: A Road Map for Researchers, Activists, and
Policymakers to Empower Data Producers [14.392208044851976]
Data producers have little say in what data is captured, how it is used, or who it benefits.
Organizations with the ability to access and process this data, e.g. OpenAI and Google, possess immense power in shaping the technology landscape.
By synthesizing related literature that reconceptualizes the production of data for computing as data labor'', we outline opportunities for researchers, policymakers, and activists to empower data producers.
arXiv Detail & Related papers (2023-05-22T17:11:22Z) - Incorporating Rivalry in Reinforcement Learning for a Competitive Game [65.2200847818153]
This work proposes a novel reinforcement learning mechanism based on the social impact of rivalry behavior.
Our proposed model aggregates objective and social perception mechanisms to derive a rivalry score that is used to modulate the learning of artificial agents.
arXiv Detail & Related papers (2022-08-22T14:06:06Z) - Mechanisms that Incentivize Data Sharing in Federated Learning [90.74337749137432]
We show how a naive scheme leads to catastrophic levels of free-riding where the benefits of data sharing are completely eroded.
We then introduce accuracy shaping based mechanisms to maximize the amount of data generated by each agent.
arXiv Detail & Related papers (2022-07-10T22:36:52Z) - Cooperative Artificial Intelligence [0.0]
We argue that there is a need for research on the intersection between game theory and artificial intelligence.
We discuss the problem of how an external agent can promote cooperation between artificial learners.
We show that the resulting cooperative outcome is stable in certain games even if the planning agent is turned off.
arXiv Detail & Related papers (2022-02-20T16:50:37Z) - Data Sharing Markets [95.13209326119153]
We study a setup where each agent can be both buyer and seller of data.
We consider two cases: bilateral data exchange (trading data with data) and unilateral data exchange (trading data with money)
arXiv Detail & Related papers (2021-07-19T06:00:34Z) - Incorporating Rivalry in Reinforcement Learning for a Competitive Game [65.2200847818153]
This study focuses on providing a novel learning mechanism based on a rivalry social impact.
Based on the concept of competitive rivalry, our analysis aims to investigate if we can change the assessment of these agents from a human perspective.
arXiv Detail & Related papers (2020-11-02T21:54:18Z) - Human Trajectory Forecasting in Crowds: A Deep Learning Perspective [89.4600982169]
We present an in-depth analysis of existing deep learning-based methods for modelling social interactions.
We propose two knowledge-based data-driven methods to effectively capture these social interactions.
We develop a large scale interaction-centric benchmark TrajNet++, a significant yet missing component in the field of human trajectory forecasting.
arXiv Detail & Related papers (2020-07-07T17:19:56Z) - Mechanisms of intermediary platforms [0.0]
This article looks at how Matchmakers and coordination networks gain market dominance.
Considering strategic and business challenges, we suggest a possible solution and strategy.
We present a cooperative approach towards a fair and open Economy of Things (EoT) based on decentralized technologies.
arXiv Detail & Related papers (2020-05-05T12:55:57Z) - Recognizing Affiliation: Using Behavioural Traces to Predict the Quality
of Social Interactions in Online Games [26.131859388185646]
We use behavioural traces to predict affiliation between dyadic strangers, facilitated through their social interactions in an online gaming setting.
We collected audio, video, in-game, and self-report data from 23 dyads, extracted 75 features, trained Random Forest and Support Vector Machine models, and evaluated their performance predicting binary (high/low) as well as continuous affiliation toward a partner.
Our findings can inform the design of multiplayer games and game communities, and guide the development of systems for matchmaking and mitigating toxic behaviour in online games.
arXiv Detail & Related papers (2020-03-06T20:56:05Z) - I Know Where You Are Coming From: On the Impact of Social Media Sources
on AI Model Performance [79.05613148641018]
We will study the performance of different machine learning models when being learned on multi-modal data from different social networks.
Our initial experimental results reveal that social network choice impacts the performance.
arXiv Detail & Related papers (2020-02-05T11:10:44Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.