In social decision-making among strategic agents, a universal focus lies on the balance between social and individual interests. Socially efficient mechanisms are thus desirably designed to not only maximize the social welfare but also incentivize the agents for their own profit. Under a generalized model that includes applications such as double auctions and trading networks, this study establishes a socially efficient (SE), dominant-strategy incentive compatible (DSIC), and individually rational (IR) mechanism with the minimum total budget expensed to the agents. The present method exploits discrete and known type domains to reduce a set of constraints into the shortest path problem in a weighted graph. In addition to theoretical derivation, we substantiate the optimality of the proposed mechanism through numerical experiments, where it certifies strictly lower budget than Vickery-Clarke-Groves (VCG) mechanisms for a wide class of instances.

Decision Transformer (DT), as one of the representative Reinforcement Learning via Supervised Learning (RvS) methods, has achieved strong performance in offline learning tasks by leveraging the powerful Transformer architecture for sequential decision-making. However, in adversarial environments, these methods can be non-robust, since the return is dependent on the strategies of both the decision-maker and adversary. Training a probabilistic model conditioned on observed return to predict action can fail to generalize, as the trajectories that achieve a return in the dataset might have done so due to a weak and suboptimal behavior adversary. To address this, we propose a worst-case-aware RvS algorithm, the Adversarial Robust Decision Transformer (ARDT), which learns and conditions the policy on in-sample minimax returns-to-go. ARDT aligns the target return with the worst-case return learned through minimax expectile regression, thereby enhancing robustness against powerful test-time adversaries. In experiments conducted on sequential games with full data coverage, ARDT can generate a maximin (Nash Equilibrium) strategy, the solution with the largest adversarial robustness. In large-scale sequential games and continuous adversarial RL environments with partial data coverage, ARDT demonstrates significantly superior robustness to powerful test-time adversaries and attains higher worst-case returns compared to contemporary DT methods.

Modeling Socioeconomic Systems: ABMs Socioeconomic systems exhibit intricate patterns of interaction and adaptation, e.g., between social groups and to changing environmental and economic conditions, reflecting a degree of complexity that challenges current methods for analysis and prediction of these systems [1, 2]. Modeling the complexity of these systems often requires detailed knowledge of the interacting components, their associated scales, and sufficient resolution of these details such that bottom-up emergent properties can be observed. Agent-based models (ABMs) are a computational modeling tool to resolve these facets of complex social science problems. ABMs simulate a large number of agents in a shared environment: through agent-to-agent and agent-environment interactions, ABMs relate system-wide emergent properties to individual behaviors [3]. This work was supported by the Multisector Dynamics program area of the U.S. Department of Energy, Office of Science, Office of Biological and Environmental Research as part of the multi-program, collaborative Integrated Coastal Modeling (ICoM) project at PNNL, a multi-program national laboratory operated by Battelle for the U.S. Department of Energy under contract DE-AC05-76RL01830.

However, a critical gap exists in the lack of generalizable and accurate specialized measurement tools Trust plays a crucial role not only in fostering cooperation, for assessing affective trust in the context of AI, especially efficiency, and productivity in human relationships (Brainov with the enhanced and nuanced capabilities of LLMs. This and Sandholm 1999) but also is essential for the effective highlights a need for a better measurement scale for affective use and acceptance of computing and automated systems, trust to gain a deeper understanding of how trust dynamics including computers (Madsen and Gregor 2000), automation function, particularly in the context of emotionally intelligent (Lee and See 2004), robots (Hancock et al. 2011), and AI. AI technologies (Kumar 2021), with a deficit in trust potentially In this paper, we introduce a 27-item semantic differential causing rejection of these technologies (Glikson and scale for assessing cognitive and affective trust in AI, Woolley 2020). The two-dimensional model of trust, encompassing aiding researchers and designers in understanding and improving both cognitive and affective dimensions proposed human-AI interactions. Our motivation and scale and studied in interpersonal relationship studies (McAllister development process is based on a long strand of prior research 1995; Johnson and Grayson 2005; Parayitam and Dooley on the cognitive-affective construct of trust that has 2009; Morrow Jr, Hansen, and Pearson 2004), have been shown to be important in interpersonal trust in organizations, been adopted in studying trust in human-computer interactions, human trust in conventional technology and automation, particularly with human-like technologies (Hu, Lu and more recently in trust towards AI.

Recent advances in large language models (LLMs) have opened new avenues for applying multi-agent systems in very large-scale simulations. However, there remain several challenges when conducting multi-agent simulations with existing platforms, such as limited scalability and low efficiency, unsatisfied agent diversity, and effort-intensive management processes. To address these challenges, we develop several new features and components for AgentScope, a user-friendly multi-agent platform, enhancing its convenience and flexibility for supporting very large-scale multi-agent simulations. Specifically, we propose an actor-based distributed mechanism as the underlying technological infrastructure towards great scalability and high efficiency, and provide flexible environment support for simulating various real-world scenarios, which enables parallel execution of multiple agents, centralized workflow orchestration, and both inter-agent and agent-environment interactions among agents. Moreover, we integrate an easy-to-use configurable tool and an automatic background generation pipeline in AgentScope, simplifying the process of creating agents with diverse yet detailed background settings. Last but not least, we provide a web-based interface for conveniently monitoring and managing a large number of agents that might deploy across multiple devices. We conduct a comprehensive simulation to demonstrate the effectiveness of the proposed enhancements in AgentScope, and provide detailed observations and discussions to highlight the great potential of applying multi-agent systems in large-scale simulations. The source code is released on GitHub at https://github.com/modelscope/agentscope to inspire further research and development in large-scale multi-agent simulations.

The Virtual Experimental Research Assistant (VERA) is an inquiry-based learning environment that empowers a learner to build conceptual models of complex ecological systems and experiment with agent-based simulations of the models. This study investigates the convergence of cognitive AI and generative AI for self-explanation in interactive AI agents such as VERA. From a cognitive AI viewpoint, we endow VERA with a functional model of its own design, knowledge, and reasoning represented in the Task--Method--Knowledge (TMK) language. From the perspective of generative AI, we use ChatGPT, LangChain, and Chain-of-Thought to answer user questions based on the VERA TMK model. Thus, we combine cognitive and generative AI to generate explanations about how VERA works and produces its answers. The preliminary evaluation of the generation of explanations in VERA on a bank of 66 questions derived from earlier work appears promising.

--Game environments offer a unique opportunity for training virtual agents due to their interactive nature, which provides diverse play traces and affect labels. Despite their potential, no reinforcement learning framework incorporates human affect models as part of their observation space or reward mechanism. T o address this, we present the Affectively Framework, a set of Open-AI Gym environments that integrate affect as part of the observation space. This paper introduces the framework and its three game environments and provides baseline experiments to validate its effectiveness and potential. Video games are ideal stimuli for research in Affective Computing [1] for several reasons. Firstly, the user is free to play in many different ways, leading to diversity in their play traces and emotional experiences [2].

Contracts are the economic framework which allows a principal to delegate a task to an agent -- despite misaligned interests, and even without directly observing the agent's actions. In many modern reinforcement learning settings, self-interested agents learn to perform a multi-stage task delegated to them by a principal. We explore the significant potential of utilizing contracts to incentivize the agents. We model the delegated task as an MDP, and study a stochastic game between the principal and agent where the principal learns what contracts to use, and the agent learns an MDP policy in response. We present a learning-based algorithm for optimizing the principal's contracts, which provably converges to the subgame-perfect equilibrium of the principal-agent game. A deep RL implementation allows us to apply our method to very large MDPs with unknown transition dynamics. We extend our approach to multiple agents, and demonstrate its relevance to resolving a canonical sequential social dilemma with minimal intervention to agent rewards.

-- Predicting agents' behavior for vehicles and pedestrians is challenging due to a myriad of factors including the uncertainty attached to different intentions, inter-agent interactions, traffic (environment) rules, individual inclinations, and agent dynamics. Consequently, a plethora of neural network-driven prediction models have been introduced in the literature to encompass these intricacies to accurately predict the agent behavior . Nevertheless, many of these approaches falter when confronted with scenarios beyond their training datasets, and lack interpretability, raising concerns about their suitability for real-world applications such as autonomous driving. Moreover, these models frequently demand additional training, substantial computational resources, or specific input features necessitating extensive implementation endeavors. In response, we propose Gaussian Lane Keeping (GLK), a robust prediction method for autonomous vehicles that can provide a solid baseline for comparison when developing new algorithms and a sanity check for real-world deployment. We provide several extensions to the GLK model, evaluate it on the CitySim dataset, and show that it outperforms the neural-network based predictions. Trajectory prediction is a heavily researched topic with numerous applications including Autonomous Driving (AD). Recent efforts have focused on multi-modal and interactive prediction models [1], often utilizing deep learning to handle complex interdependencies [2]. Observations from researchers suggest that a constant velocity prediction model often provides a more robust baseline in such scenarios [4]. Moreover, many researchers and practitioners prefer to employ more reliable and computationally efficient methods for their systems [5].

This work introduces a Strategic Pseudo-Goal Perturbation (SPGP) technique, a novel approach to resolve deadlock situations in multi-agent navigation scenarios. Leveraging the robust framework of Safety Barrier Certificates, our method integrates a strategic perturbation mechanism that guides agents through social mini-games where deadlock and collision occur frequently. The method adopts a strategic calculation process where agents, upon encountering a deadlock select a pseudo goal within a predefined radius around the current position to resolve the deadlock among agents. The calculation is based on controlled strategic algorithm, ensuring that deviation towards pseudo-goal is both purposeful and effective in resolution of deadlock. Once the agent reaches the pseudo goal, it resumes the path towards the original goal, thereby enhancing navigational efficiency and safety. Experimental results demonstrates SPGP's efficacy in reducing deadlock instances and improving overall system throughput in variety of multi-agent navigation scenarios.