Emergence in Multi-Agent Systems: A Safety Perspective

TL;DR

The paper addresses emergent behavior in multi-agent systems caused by misalignment between a global intended specification $\mathcal{F}^*$ and locally approximated specifications $\hat{\mathcal{F}}$, examining how decomposition and local learning can produce unsafe or suboptimal global outcomes. It proposes a formal model combining MAS and safety concepts, and validates it with two gridworld experiments where two agents must collect targets, showing that tailoring the underlying parameterization or observation can mitigate emergent effects in planning and learning. The key contributions include a formal tracing framework for emergence in MAS, concrete remediation strategies (reward and observation adaptations), and empirical evidence that such adaptations reduce inefficiencies and prevent deadlocks. The work highlights the practical significance of specification-aware design for safer, more reliable collective adaptive systems and suggests pathways, such as RLHF and richer benchmarks, to extend these ideas to real-world MAS deployments.

Abstract

Emergent effects can arise in multi-agent systems (MAS) where execution is decentralized and reliant on local information. These effects may range from minor deviations in behavior to catastrophic system failures. To formally define these effects, we identify misalignments between the global inherent specification (the true specification) and its local approximation (such as the configuration of different reward components or observations). Using established safety terminology, we develop a framework to understand these emergent effects. To showcase the resulting implications, we use two broadly configurable exemplary gridworld scenarios, where insufficient specification leads to unintended behavior deviations when derived independently. Recognizing that a global adaptation might not always be feasible, we propose adjusting the underlying parameterizations to mitigate these issues, thereby improving the system's alignment and reducing the risk of emergent failures.

Figures (3)

• Figure 1: Emergence in MAS: Generally, we assume a global task to be solved, given by the target specification $\mathcal{F}^*$. For individual behavior (typically in the form of policies) to be derived , a local target (reward, observation, ...) needs to be formalized (typically by defining $\mathcal{M}$). Optimally, the resulting policy perfectly fulfills this previously defined local specification . However, when executed in the global context , those policies might exhibit emergent effects . By adding this new step to the MAS engineering cycle, we intend to discover emergent effects resulting from a misalignment between the real target and the resulting global behavior . We trace this effect to approximation errors induced throughout the overall development process, mainly to a miss-parameterization of the target to be optimized diverging from the intended target inherent to .
• Figure 2: Overview of our emergence evaluation environments
• Figure 3: \ref{['fig:routes_chasing']} and \ref{['fig:routes_blocking']} show one instance of the emergent behavior, whereas the routes in \ref{['fig:routes_collective']} and \ref{['fig:routes_coordination']} result from the usage of our emergence prevention approaches. The blue and purple trajectories correspond to the behavior of our RL-agents. The yellow and orange routes belong to the TSP-agents. \ref{['fig:collected_coins']} and \ref{['fig:reached_flags']} give an overview of the distribution of the required environment time steps to reach the targets considering multiple random seeds. For Collection, we randomized the positions and the number of coins on the quarter circle. For Coordination, we also plotted the confidence intervals (red whiskers).