Tab. 13 shows the parameters and variables used in this optimization. Table 13: Parameters and variables used in credit optimization.Known Parameters Description ϱ = R Eq. 5 presents the optimization formulation, where Eq. 5a calculates the total credits gained by the The following examples illustrate the prompts used in LLM-C for each mini-game. The prompts vary slightly for different mini-games and also differ across stages within the same mini-game. Specifically, the prompt for the dynamic scenario in Social Structure is presented in Listing 1. The corresponding prompts are provided in Listing 4 and Listing 5. 27 Listing 1: Prompt example for dynamic scenario in Social Structure . Instructions: - The AdaSociety game is an open-ended multi-agent environment. The game consists ofa complex crafting tree, where the agent needs to obtain as many resources aspossible in the limited time and craft tools to mine more advanced resources tomaximize its benefit. At the same time, agents can also take other actions tohelp them increase their returns. The numbers of resources are limited.- Map: AdaSociety is a 2D grid-world game. The map size is 15*15.- Some of them can only bediscovered with some specific tools, which will be introduced next.-

Tab. 13 shows the parameters and variables used in this optimization. Table 13: Parameters and variables used in credit optimization.Known Parameters Description ϱ = R Eq. 5 presents the optimization formulation, where Eq. 5a calculates the total credits gained by the The following examples illustrate the prompts used in LLM-C for each mini-game. The prompts vary slightly for different mini-games and also differ across stages within the same mini-game. Specifically, the prompt for the dynamic scenario in Social Structure is presented in Listing 1. The corresponding prompts are provided in Listing 4 and Listing 5. 27 Listing 1: Prompt example for dynamic scenario in Social Structure . Instructions: - The AdaSociety game is an open-ended multi-agent environment. The game consists ofa complex crafting tree, where the agent needs to obtain as many resources aspossible in the limited time and craft tools to mine more advanced resources tomaximize its benefit. At the same time, agents can also take other actions tohelp them increase their returns. The numbers of resources are limited.- Map: AdaSociety is a 2D grid-world game. The map size is 15*15.- Some of them can only bediscovered with some specific tools, which will be introduced next.-

The proliferation of misinformation across diverse social media platforms has drawn significant attention from both academic and industrial communities due to its detrimental effects. Accordingly, automatically distinguishing misinformation, dubbed as Misinformation Detection (MD), has become an increasingly active research topic. The mainstream methods formulate MD as a static learning paradigm, which learns the mapping between the content, links, and propagation of news articles and the corresponding manual veracity labels. However, the static assumption is often violated, since in real-world scenarios, the veracity of news articles may vacillate within the dynamically evolving social environment. To tackle this problem, we propose a novel framework, namely Misinformation detection with Dynamic Environmental Representations (MISDER). The basic idea of MISDER lies in learning a social environmental representation for each period and employing a temporal model to predict the representation for future periods. In this work, we specify the temporal model as the LSTM model, continuous dynamics equation, and pre-trained dynamics system, suggesting three variants of MISDER, namely MISDER-LSTM, MISDER-ODE, and MISDER-PT, respectively. To evaluate the performance of MISDER, we compare it to various MD baselines across 2 prevalent datasets, and the experimental results can indicate the effectiveness of our proposed model.

The exponential growth of social media and generative AI has transformed information dissemination, fostering connectivity but also accelerating the spread of misinformation. Understanding information propagation dynamics and developing effective control strategies is essential to mitigate harmful content. Traditional models, such as SIR, provide basic insights but inadequately capture the complexities of online interactions. Advanced methods, including attention mechanisms and graph neural networks, enhance accuracy but typically overlook user psychology and behavioral dynamics. Large language models (LLMs), with their human-like reasoning, offer new potential for simulating psychological aspects of information spread. We introduce an LLM-based simulation environment capturing agents' evolving attitudes, emotions, and responses. Initial experiments, however, revealed significant gaps between LLM-generated behaviors and authentic human dynamics, especially in stance detection and psychological realism. A detailed evaluation through Social Information Processing Theory identified major discrepancies in goal-setting and feedback evaluation, stemming from the lack of emotional processing in standard LLM training. To address these issues, we propose the Social Information Processing-based Chain of Thought (SIP-CoT) mechanism enhanced by emotion-guided memory. This method improves the interpretation of social cues, personalization of goals, and evaluation of feedback. Experimental results confirm that SIP-CoT-enhanced LLM agents more effectively process social information, demonstrating behaviors, attitudes, and emotions closer to real human interactions. In summary, this research highlights critical limitations in current LLM-based propagation simulations and demonstrates how integrating SIP-CoT and emotional memory significantly enhances the social intelligence and realism of LLM agents.

Traditional interactive environments limit agents' intelligence growth with fixed tasks. Recently, single-agent environments address this by generating new tasks based on agent actions, enhancing task diversity. We consider the decision-making problem in multi-agent settings, where tasks are further influenced by social connections, affecting rewards and information access. However, existing multi-agent environments lack a combination of adaptive physical surroundings and social connections, hindering the learning of intelligent behaviors. To address this, we introduce AdaSociety, a customizable multi-agent environment featuring expanding state and action spaces, alongside explicit and alterable social structures. As agents progress, the environment adaptively generates new tasks with social structures for agents to undertake. In AdaSociety, we develop three mini-games showcasing distinct social structures and tasks. Initial results demonstrate that specific social structures can promote both individual and collective benefits, though current reinforcement learning and LLM-based algorithms show limited effectiveness in leveraging social structures to enhance performance. Overall, AdaSociety serves as a valuable research platform for exploring intelligence in diverse physical and social settings.

Cultural code-switching concerns how we adjust our overall behaviours, manners of speaking, and appearance in response to a perceived change in our social environment. We defend the need to investigate cultural code-switching capacities in artificial intelligence systems. We explore a series of ethical and epistemic issues that arise when bringing cultural code-switching to bear on artificial intelligence. Building upon Dotson's (2014) analysis of testimonial smothering, we discuss how emerging technologies in AI can give rise to epistemic oppression, and specifically, a form of self-silencing that we call 'cultural smothering'. By leaving the socio-dynamic features of cultural code-switching unaddressed, AI systems risk negatively impacting already-marginalised social groups by widening opportunity gaps and further entrenching social inequalities.

This paper attempts to synthesize various conceptualizations of the term "context" as found in computing literature. Ten conceptual dimensions of context thus emerge -- location; user, task, and system characteristics; physical, social, organizational, and cultural environments; time-related aspects, and historical information. Together, the ten dimensions of context provide a comprehensive view of the notion of context, and allow for a more systematic examination of the influence of context and contextual information on human-system or human-AI interactions.

Agent based simulation of social organizations, via the investigation of agents' training and learning tactics and strategies, has been inspired by the ability of humans to learn from social environments which are rich in agents, interactions and partial or hidden information. Such richness is a source of complexity that an effective learner has to be able to navigate. This paper focuses on the investigation of the impact of the environmental complexity on the game playing-and-learning behavior of synthetic agents. We demonstrate our approach using two independent turn-based zero-sum games as the basis of forming social events which are characterized both by competition and cooperation. The paper's key highlight is that as the complexity of a social environment changes, an effective player has to adapt its learning and playing profile to maintain a given performance profile

Recently, new ideas about'artificial intelligence' and'cognitive computing systems' in education have been advanced by major computing and educational businesses. More particularly, what understandings of the human teacher and the learner are assumed in the development of such systems, and with what potential effects? The focus here is on the education business Pearson, which published a report entitled Intelligence Unleashed: An argument for AI in education in February 2016, and the computing company IBM, which launched Personalized Education: from curriculum to career with cognitive systems in May 2016. Pearson's interest in AI reflects its growing profile as an organization using advanced forms of data analytics to measure educational institutions and practices while IBM's report on cognitive systems makes a case for extending its existing R&D around cognitive computing into the education sector. AI has been the subject of serious concern recently, with warnings from high-profile figures including Stephen Hawking, Bill Gates and Elon Musk, while awareness about cognitive computing has been fuelled by widespread media coverage of Google's AlphaGo system, which beat one of the world's leading Go players back in March. Commenting on these recent events, the philosopher Luciano Floridi has noted that contemporary AI and cognitive computing, however, cannot be characterized in monolithic terms as some kind of'ultraintelligence'; instead it is manifesting itself in far more mundane ways through an'infosphere' of'ordinary artefacts that outperform us in ever more tasks, despite being no cleverer than a toaster': The success of our technologies depends largely on the fact that, while we were speculating about the possibility of ultraintelligence, we increasingly enveloped the world in so many devices, sensors, applications and data that it became an IT-friendly environment, where technologies can replace us without having any understanding, mental states, intentions, interpretations, emotional states, semantic skills, consciousness, self-awareness or flexible intelligence.

This paper analyses the influence of including agents of different degrees of intelligence in a multiagent system. The goal is to better understand how we can develop intelligence tests that can evaluate social intelligence. We analyse several reinforcement algorithms in several contexts of cooperation and competition. Our experimental setting is inspired by the recently developed Darwin-Wallace distribution.