Tootfinder

This is really nice: I can finally share the news that the #OAPEN Library and #DOAB will be hosted by #CERN
"An expanded collaboration agreement between CERN and the OAPEN Foundation sees the Laborat…

NLP4Gov: A Comprehensive Library for Computational Policy Analysis
Mahasweta Chakraborti, Sailendra Akash Bonagiri, Santiago Virg\"uez-Ruiz, Seth Frey
https://arxiv.org/abs/2404.03206

NLP4Gov: A Comprehensive Library for Computational Policy Analysis
Formal rules and policies are fundamental in formally specifying a social system: its operation, boundaries, processes, and even ontology. Recent scholarship has highlighted the role of formal policy in collective knowledge creation, game communities, the production of digital public goods, and national social media governance. Researchers have shown interest in how online communities convene tenable self-governance mechanisms to regulate member activities and distribute rights and privileges b…

Synergy as the failure of distributivity
Ivan A. Sevostianov, Ofer Feinerman
https://arxiv.org/abs/2404.03455 https://arxiv.org/pdf/2…

Synergy as the failure of distributivity
A physical system is synergistic if it cannot be reduced to its constituents. Intuitively this is paraphrased into the common statement that 'the whole is greater than the sum of its parts'. In this manner, many basic parts in combination may give rise to some unexpected collective behavior. A paradigmatic example of such phenomenon is information. Several sources, which are already known individually, may provide some new knowledge when joined together. Here we take the trivial case of discret…

Operational Collective Intelligence of Humans and Machines
Nikolos Gurney, Fred Morstatter, David V. Pynadath, Adam Russell, Gleb Satyukov
https://arxiv.org/abs/2402.13273

Operational Collective Intelligence of Humans and Machines
We explore the use of aggregative crowdsourced forecasting (ACF) as a mechanism to help operationalize ``collective intelligence'' of human-machine teams for coordinated actions. We adopt the definition for Collective Intelligence as: ``A property of groups that emerges from synergies among data-information-knowledge, software-hardware, and individuals (those with new insights as well as recognized authorities) that enables just-in-time knowledge for better decisions than these three elements a…

NLP4Gov: A Comprehensive Library for Computational Policy Analysis
Mahasweta Chakraborti, Sailendra Akash Bonagiri, Santiago Virg\"uez-Ruiz, Seth Frey
https://arxiv.org/abs/2404.03206

NLP4Gov: A Comprehensive Library for Computational Policy Analysis
Formal rules and policies are fundamental in formally specifying a social system: its operation, boundaries, processes, and even ontology. Recent scholarship has highlighted the role of formal policy in collective knowledge creation, game communities, the production of digital public goods, and national social media governance. Researchers have shown interest in how online communities convene tenable self-governance mechanisms to regulate member activities and distribute rights and privileges b…

Platforms for Efficient and Incentive-Aware Collaboration
Nika Haghtalab, Mingda Qiao, Kunhe Yang
https://arxiv.org/abs/2402.15169 https://

Platforms for Efficient and Incentive-Aware Collaboration
Collaboration is crucial for reaching collective goals. However, its effectiveness is often undermined by the strategic behavior of individual agents -- a fact that is captured by a high Price of Stability (PoS) in recent literature [Blum et al., 2021]. Implicit in the traditional PoS analysis is the assumption that agents have full knowledge of how their tasks relate to one another. We offer a new perspective on bringing about efficient collaboration among strategic agents using information de…

A Game-Theoretic Analysis of Auditing Differentially Private Algorithms with Epistemically Disparate Herd
Ya-Ting Yang, Tao Zhang, Quanyan Zhu
https://arxiv.org/abs/2404.16195

A Game-Theoretic Analysis of Auditing Differentially Private Algorithms with Epistemically Disparate Herd
Privacy-preserving AI algorithms are widely adopted in various domains, but the lack of transparency might pose accountability issues. While auditing algorithms can address this issue, machine-based audit approaches are often costly and time-consuming. Herd audit, on the other hand, offers an alternative solution by harnessing collective intelligence. Nevertheless, the presence of epistemic disparity among auditors, resulting in varying levels of expertise and access to knowledge, may impact au…

This https://arxiv.org/abs/2305.06733 has been replaced.
initial toot: https://mastoxiv.page/@arX…

Modeling collective behaviors from optic flow and retinal cues
Animal collective behavior is often modeled with self-propelled particles, assuming each individual has ``omniscient'' knowledge of its neighbors. Yet, neighbors may be hidden from view and we do not know the effect of this information loss. To address this question, we propose a visual model of collective behavior where each particle moves according to bio-plausible visual cues, in particular the optic flow. This visual model successfully reproduces three classical collective behaviors: swarmi…

Aligning Individual and Collective Objectives in Multi-Agent Cooperation
Yang Li, Wenhao Zhang, Jianhong Wang, Shao Zhang, Yali Du, Ying Wen, Wei Pan
https://arxiv.org/abs/2402.12416

Aligning Individual and Collective Objectives in Multi-Agent Cooperation
In the field of multi-agent learning, the challenge of mixed-motive cooperation is pronounced, given the inherent contradictions between individual and collective goals. Current research in this domain primarily focuses on incorporating domain knowledge into rewards or introducing additional mechanisms to foster cooperation. However, many of these methods suffer from the drawbacks of manual design costs and the lack of a theoretical grounding convergence procedure to the solution. To address th…

Evolutionary Optimization of Model Merging Recipes
Takuya Akiba, Makoto Shing, Yujin Tang, Qi Sun, David Ha
https://arxiv.org/abs/2403.13187 https://

Evolutionary Optimization of Model Merging Recipes
We present a novel application of evolutionary algorithms to automate the creation of powerful foundation models. While model merging has emerged as a promising approach for LLM development due to its cost-effectiveness, it currently relies on human intuition and domain knowledge, limiting its potential. Here, we propose an evolutionary approach that overcomes this limitation by automatically discovering effective combinations of diverse open-source models, harnessing their collective intellige…

This https://arxiv.org/abs/2310.03492 has been replaced.
initial toot: https://mastoxiv.page/@ar…

Analyzing multimodal probability measures with autoencoders
Finding collective variables to describe some important coarse-grained information on physical systems, in particular metastable states, remains a key issue in molecular dynamics. Recently, machine learning techniques have been intensively used to complement and possibly bypass expert knowledge in order to construct collective variables. Our focus here is on neural network approaches based on autoencoders. We study some relevant mathematical properties of the loss function considered for traini…