Optimizing the efficiency of collective decision making in groups

The complexity of modern military operations create a demand for efficient collaborative decision making and problem solving. Additionally, as military units operate in increasingly dynamic environments, the ability to respond to changing circumstances becomes paramount for mission success. An effective response rests on correct dissemination and transfer of information across the command and control structure, and thus is critically linked to the network of human interactions. In this paper, we take an agent-based modeling approach to collective problem solving. We investigate three key factors affecting the performance in collaborative environments: (1) the structure of network used to share information between agents, (2) the search strategies adopted by agents, and (3) the complexity of problems facing the group. In particular we study how the trade-off between exploitation of known solutions and exploration for novel ones is related to the efficiency of collective search. Additionally we consider the role of agent behavior: propensity for risk-taking and trustworthiness, as well as the dynamic nature of social connections. Finally, we outline the directions for future work regarding the efficiency of problem solving on military-like command and control structures

Variational Exploration Module VEM: A Cloud-Native Optimization and Validation Tool for Geospatial Modeling and AI Workflows

Geospatial observations combined with computational models have become key to understanding the physical systems of our environment and enable the design of best practices to reduce societal harm. Cloud-based deployments help to scale up these modeling and AI workflows. Yet, for practitioners to make robust conclusions, model tuning and testing is crucial, a resource intensive process which involves the variation of model input variables. We have developed the Variational Exploration Module which facilitates the optimization and validation of modeling workflows deployed in the cloud by orchestrating workflow executions and using Bayesian and machine learning-based methods to analyze model behavior. User configurations allow the combination of diverse sampling strategies in multi-agent environments. The flexibility and robustness of the model-agnostic module is demonstrated using real-world applications.Comment: Submitted to IAAI 2024: Deployed Innovative Tools for Enabling AI Application

A Horizon Scan of Emerging Global Biological Conservation Issues for 2020.

In this horizon scan, we highlight 15 emerging issues of potential relevance to global conservation in 2020. Seven relate to potentially extensive changes in vegetation or ecological systems. These changes are either relatively new, for example, conversion of kelp forests to simpler macroalgal systems, or may occur in the future, for example, as a result of the derivation of nanocelluose from wood or the rapid expansion of small hydropower schemes. Other topics highlight potential changes in national legislation that may have global effect on international agreements. Our panel of 23 scientists and practitioners selected these issues using a modified version of the Delphi technique from a long-list of 89 potential topics.NERC and RSPB fundin