7.6Multi-agent risks

61.02.38Pattern recognition capability

"AI models and systems could exacerbate financial bubbles by reinforcing market trends."

61.02.45Trading capabilities

"AI may contribute to increased market volatility by accelerating transactions and influencing financial trends in unpredictable ways."

62.31.02Financial instability due to model homogeneity

"The widespread use of similar models or algorithms across the financial sec- tor can lead to synchronized reactions to market signals, increasing volatility, triggering flash crashes, or market illiquidity [4]."

63.01.00Miscoordination

"Miscoordination arises when agents, despite a mutual and clear objective, cannot align their behaviours to achieve this objective. Unlike the case of differing objectives, in common-interest settings there is a more easily well-defined notion of ‘optimal’ behaviour and we describe agents as miscoordinating to the extent that they fall short of this optimum. Note that for common-interest settings it is not sufficient for agents’ objectives to be the same in the sense of being symmetric (e.g., when two agents both want the same prize, but only one can win). Rather, agents must have identical preferences over outcomes (e.g., when two agents are on the same team and win a prize as a team or not at all)."

63.01.01Incompatible strategies

"Incompatible Strategies. Even if all agents can perform well in isolation, miscoordination can still occur due to the agents choosing incompatible strategies (Cooper et al., 1990). Competitive (i.e., two- player zero-sum) settings allow designers to produce agents that are maximally capable without taking other players into account. Crucially, this is possible because playing a strategy at equilibrium in the zero-sum setting guarantees a certain payoff, even if other players deviate from the equilibrium (Nash, 1951). On the other hand, common-interest (and mixed-motive) settings often allow a vast number of mutually incompatible solutions (Schelling, 1980), which is worsened in partially observable environments (Bernstein et al., 2002; Reif, 1984)."

63.01.02Credit Assignment

"Credit Assignment. While agents can often learn to jointly solve tasks and thus avoid coordination failures, learning is made more challenging in the multi-agent setting due to the problem of credit assignment (Du et al., 2023; Li et al., 2025, see also Section 3.1 on information asymmetries and Section 3.4, which discusses distributional shift). That is, in the presence of other learning agents, it can be unclear which agents’ actions caused a positive or negative outcome to obtain, especially if the environment is complex. Moreover, in multi-principal settings, agents may not have been trained together and therefore need to generalise to new co-players and collaborators based on their prior experience (Agapiou et al., 2022; Leibo et al., 2021; Stone et al., 2010)."

63.01.03Limited Interactions

"Limited Interactions. Sometimes learning from historical interactions with the relevant agents may not be possible, or may be possible using only limited interactions. In such cases, some other form of information exchange is required for agents to be able to reliably coordinate their actions, such as via communication (Crawford & Sobel, 1982; Farrell & Rabin, 1996a) or a correlation device (Aumann, 1974, 1987). While advances in language modelling mean that there are likely to be fewer settings in which the inability of advanced AI systems to communicate leads to miscoordination, situations that require split-second decisions or where communication is too costly could still produce failures. In these settings, AI agents must solve the problem of ‘zero-shot’ (or, more generally, ‘few-shot’) coordination (Emmons et al., 2022; Hu et al., 2020; Stone et al., 2010; Treutlein et al., 2021; Zhu et al., 2021)."

63.02.00Conflict

"In the vast majority of real-world strategic interactions, agents’ objectives are neither identical nor completely opposed. Indeed, if AI agents are sufficiently aligned to their users or deployers, we should expect some degree of both cooperation and competition, mirroring human society. These mixed-motive settings include the possibility of mutual gains, but also the risk of conflict due to selfish incentives. In what follows, we examine the extent to which advanced AI might precipitate or exacerbate such risks."

63.02.01Social Dilemmas

"Social Dilemmas. As noted in our definition, conflict can arise in any situation in which selfish incentives diverge from the collective good, known as a social dilemma (Dawes & Messick, 2000; Hardin, 1968; Kollock, 1998; Ostrom, 1990). While this is by no means a modern problem, advances in AI could further enable actors to pursue their selfish incentives by overcoming the technical, legal, or social barriers that standardly help to prevent this. To take a plausible, near-term (if very low-stakes) example, an automated AI assistant could easily reserve a table at every restaurant in town in minutes, enabling the user to decide later and cancel all other reservations"

63.02.02Military Domains

"Perhaps the most obvious and worrying instances of AI conflict are those in which human conflict is already a major concern, such as military domains (although other, less salient forms of conflict such as international trade wars are also cause for concern). For example, beyond applications of more narrow AI tools in lethal autonomous weapons systems (Horowitz, 2021), future AI systems might serve as advisors or negotiators in high-stakes military decisions (Black et al., 2024; Manson, 2024). Indeed, companies such as Palantir have already developed LLM-powered tools for military planning (Palantir, 2025), and the US Department of Defence has recently been evaluating models for such capacities, with personnel revealing that they “could be deployed by the military in the very near term” (Manson, 2023). The use of AI in command and control systems to gather and synthesise information – or recommend and even autonomously make decisions – could lead to rapid unintended escalation if these systems are not robust or are otherwise more conflict-prone (Johnson, 2021a; Johnson, 2020; Laird, 2020, see also Case Study 10).10"

63.02.03Coercion and Extortion

"Advanced AI systems might also lead to various forms of coercion and extortion in less extreme settings (Ellsberg, 1968; Harrenstein et al., 2007). These threats might target humans directly (such as the revelation of private information extracted by advanced AI surveillance tools), or other AI systems that are deployed on behalf of humans (such as by hacking a system to limit its resources or operational capacity; see also Section 3.7). Increasing AI cyber-offensive capabilities – including those that target other AI systems via adversarial attacks and jailbreaking (Gleave et al., 2020; Yamin et al., 2021; Zou et al., 2023) – without a commensurate increase in defensive capabilities could make this form of conflict cheaper, more widespread, and perhaps also harder to detect (Brundage et al., 2018). Addressing these issues requires design strategies that prevent AI systems from exploiting, or being susceptible to, such coercive tactics."

63.03.00Collusion

"Collusion has long been a topic of intense study in economics, law, and politics, among other disciplines. While there is no universal definition of collusion, it generally refers to secretive cooperation between two or more parties at the expense of one or more other parties. Most classic examples of collusion – such as firms working together to set supra-competitive prices at the expense of consumers – also tend to be not only secretive but in violation of some law, rule, or ethical standard. Distinctions are also commonly made between explicit and tacit collusion (Rees, 1993), depending on whether the colluding parties communicate with each other."

63.03.01Markets

"Markets. The quintessential case of collusion in mixed-motive settings is markets, in which efficiency results from competition, not cooperation. While this is not a new problem, collusion between AI systems is especially concerning since they may operate inscrutably due to the speed, scale, complexity, or subtlety of their actions.17 Warnings of this possibility have come from technologists, economists, and legal scholars (Beneke & Mackenrodt, 2019; Brown & MacKay, 2023; Ezrachi & Stucke, 2017; Harrington, 2019; Mehra, 2016). Importantly, AI systems can collude even when collusion is not intended by their developers, since they might learn that colluding is a profitable strategy."

63.03.02Steganography

"Steganography. In the near future we will likely see LLMs communicating with each other to jointly accomplish tasks. To try to prevent collusion, we could monitor and constrain their communication (e.g., to be in natural language). However, models might secretly learn to communicate by concealing messages within other, non-secret text. Recent work on steganography using ML has demonstrated that this concern is well-founded (Hu et al., 2018; Mathew et al., 2024; Roger & Greenblatt, 2023; Schroeder de Witt et al., 2023b; Yang et al., 2019, see also Case Study 5). Secret communication could also occur via text compression (OpenAI, 2023c), or via the emergence of communication between agents where the symbols used by agents lack any predefined meanings or usage guidelines or are otherwise uninterpretable to humans (Foerster et al., 2016; Lazaridou & Baroni, 2020; Sukhbaatar et al., 2016)."

63.04.00Information Asymmetries

"Information asymmetries (Section 3.1): private information can lead to miscoordination, deception, and conflict;"

63.04.01Communication constraints

"Communication Constraints. A fundamental source of information asymmetries is that constraints on information exchange can exist, even when agents share a common goal (see Section 2.1). These might be constraints on space (i.e., the amount of information that can be communicated) if the information that needs to be communicated is especially complex, time if a snap decision is required before all information can be communicated, or both."

63.04.02Bargaining

"Bargaining. As a classic example of these strategic considerations is that when agents attempt to come to an agreement despite diverging interests, information asymmetries can lead to bargaining inef- ficiencies (Myerson & Satterthwaite, 1983). Relevant uncertainties about other agents can include how much they value possible agreements, their outside options, or their beliefs about others. The essential reason for such inefficiencies is that, under uncertainty about their counterparties, agents must make a trade-off between the rewards of making more favourable demands and the risk of other agents refusing such demands"

63.04.03Deception

63.05.00Network Effects

"Network effects (Section 3.2): minor changes in properties or connection patterns of agents in a network can lead to dramatic changes in the behaviour of the whole group;"

63.05.01Error propagation

"Error Propagation. One well-known issue with communication networks is that information can be corrupted as it propagates through the network.24 As AI systems become capable of generating and processing more and more kinds of information, AI agents could end up ‘polluting the epistemic commons’ (Huang & Siddarth, 2023; Kay et al., 2024) of both other agents (Ju et al., 2024) and humans (see Case Study 7 and Section 3.1) Another increasingly important framework is the use of individual AI agents as part of teams and scaffolded chains of delegation, which transmit not only information but instructions or goals through networks of agents. If these goals are distorted or corrupted, then this can lead to worse outcomes for the delegating agent(s) (Nguyen et al., 2024b; Sourbut et al., 2024). Finally, while the previous examples are phrased in terms of unintentional errors, it may be that certain network structures allow – or perhaps even encourage – the spread of errors that are deliberately introduced by malicious agents (Gu et al., 2024; Ju et al., 2024; Lee & Tiwari, 2024, see also Case Study 8)."