Beyond Self-Interest: Modeling Social-Oriented Motivation for Human-like Multi-Agent Interactions

Abstract

Large Language Models (LLMs) demonstrate significant potential for generating complex behaviors, yet most approaches lack mechanisms for modeling social motivation in human-like multi-agent interaction. We introduce Autonomous Social Value-Oriented agents (ASVO), where LLM-based agents integrate desire-driven autonomy with Social Value Orientation (SVO) theory. At each step, agents first update their beliefs by perceiving environmental changes and others' actions. These observations inform the value update process, where each agent updates multi-dimensional desire values through reflective reasoning and infers others' motivational states. By contrasting self-satisfaction derived from fulfilled desires against estimated others' satisfaction, agents dynamically compute their SVO along a spectrum from altruistic to competitive, which in turn guides activity selection to balance desire fulfillment with social alignment. Experiments across School, Workplace, and Family contexts demonstrate substantial improvements over baselines in behavioral naturalness and human-likeness. These findings show that structured desire systems and adaptive SVO drift enable realistic multi-agent social simulations.

Figures (6)

• Figure 1: Upper-left: Static Personality agents condition actions on fixed goals. Upper-right: Desire-Driven agents produce human-like behavior by grounding decisions in internal desires. Lower: SVO-Driven agents (ours) build upon desire-based autonomy by incorporating Social Value Orientation and explicitly weighing each action’s impact on both self and others’ anticipated satisfaction, thereby moving beyond pure self-interest toward a richer model of human social behavior.
• Figure 2: Overview of the ASVO framework. During the Initialization stage, each agent’s Individual Profile is constructed, specifying its social personality type (expected SVO), a set of core psychological desires, and agent-specific components including Observation, Memory, Background Knowledge, Identity, and Goal. These attributes are encoded into the agent’s internal state, forming the basis of simulation. At each simulation step, the pipeline iteratively proceeds through four key modules: Belief Update (agents incorporate shared environment contexts and summarize observations to update beliefs about others’ states), Value Update (updating the Internal Desire State via self-reflection and the External Desire State by estimating others’ needs, including unobserved agents), SVO Calculation (computing self and others’ satisfaction to compute the agent’s current SVO with an interval check), and Activity Generation (proposing, evaluating, and selecting LLM-driven activities by synthesizing desires, context, and SVO). This forms a closed feedback loop over a text-based environment, enabling the co-evolution of motivations and social values throughout multi-agent social interaction.
• Figure 3: Behavioral distributions of four SVO-based social personality types, Altruistic, Prosocial, Individualistic, and Competitive, showing proportions of cooperation, competition, and intermediate behaviors across frameworks.
• Figure 4: Distribution of cooperative and competitive actions for each social personality type within the School context across micro-, meso-, and macro-level scales.
• Figure 5: Temporal evolution of SVO mean and standard deviation for each personality type. Shaded areas denote the reference range of each SVO category, with values remaining within their respective intervals across simulation steps.