Beyond Agreement: How Humans and AI Can Build Shared Understanding

by

Author: Denis Avetisyan

A new approach to human-AI collaboration focuses on jointly constructing causal models of the world, rather than simply aligning behaviors.

This review proposes ‘collaborative causal sensemaking’ as a framework for epistemic alignment in multi-agent systems, moving beyond behavioral alignment towards shared world and goal models.

Despite rapid advances in AI, integrating large language models into expert decision-making often fails to yield synergistic improvements, with human-AI teams frequently underperforming individual experts. This paper, ‘Collaborative Causal Sensemaking: Closing the Complementarity Gap in Human-AI Decision Support’, argues this isn’t simply a matter of accuracy, but a fundamental disconnect in how AI assistance is conceived-a lack of shared cognitive process. We propose a shift toward ‘collaborative causal sensemaking,’ where agents actively participate in co-constructing, critiquing, and revising world and goal models with humans. Could reframing multi-agent systems research around agents that think with their human partners unlock genuinely complementary intelligence?

Deconstructing Shared Understanding: The Foundation of Collective Intelligence

Truly effective collaboration between artificial agents hinges on more than simply exchanging data; it demands the construction of shared models – internal representations of the environment, the tasks at hand, and crucially, each other’s beliefs, goals, and likely actions. These models aren’t static blueprints but dynamic frameworks constantly updated through observation and interaction. An agent cannot anticipate a teammate’s move, or effectively coordinate a complex action, without a reasonable approximation of what that teammate knows, wants, and expects. This capacity for ‘theory of mind’ – mirroring the cognitive ability found in social animals – allows agents to predict behavior, resolve ambiguity, and ultimately, achieve synergistic outcomes that would be impossible with purely informational exchange. Without these shared models, agents operate in isolated silos, prone to miscommunication and inefficient, uncoordinated action.

Conventional multi-agent systems frequently encounter difficulties when attempting to construct and maintain a cohesive understanding of their surroundings, particularly as those surroundings evolve. Early methods often relied on static representations or simplistic models, proving inadequate when faced with unpredictable changes or complex interactions. The challenge lies in the computational burden of continuously updating shared models – each agent’s perception and knowledge must be reconciled with others, a process that becomes exponentially more difficult with increasing numbers of agents or environmental volatility. Furthermore, representing nuanced concepts like belief, intention, and uncertainty within a shared model requires sophisticated frameworks that can handle incomplete information and potential discrepancies between agents’ perspectives, a task that has historically proven computationally expensive and prone to errors in dynamic settings.

The absence of a common ground in multi-agent systems precipitates a cascade of detrimental effects on collaborative performance. When agents operate with differing interpretations of the environment, goals, or each other’s capabilities, even seemingly straightforward tasks become burdened by inefficiencies; actions require excessive communication to confirm assumptions, and redundant efforts frequently occur. More critically, these discrepancies foster misunderstandings, leading to conflicting actions and potentially jeopardizing the entire collaborative endeavor. Consequently, the collective outcome consistently falls short of its potential, as agents are unable to synergize their efforts effectively – a phenomenon that highlights the fundamental importance of establishing robust mechanisms for shared understanding in any complex, multi-agent system.

Causal Sensemaking: Architecting Collaboration Through Shared Models

Collaborative Causal Sensemaking is a framework wherein multiple agents iteratively build and refine shared causal models to achieve a common understanding of a task or environment. This process involves agents exchanging observations and hypotheses about causal relationships, leading to a joint representation of how different variables interact. The framework supports not only identifying direct causal links – $X \rightarrow Y$ – but also reasoning about interventions and counterfactuals to predict the outcomes of actions and understand the consequences of events. By constructing these shared models, agents can improve their individual performance and coordinate their actions more effectively, particularly in complex or uncertain situations where a single agent’s perspective may be incomplete or inaccurate.

Constructivist Collaborative Playworlds (CCPs) are simulated environments designed to facilitate the development of iterative causal sensemaking skills in artificial agents. These platforms allow multiple agents to interact with a shared virtual world, collaboratively manipulating objects and observing the resulting effects to build and refine their understanding of causal relationships. CCPs emphasize active learning through experimentation and communication, where agents must not only discover causal links but also share and reconcile their individual beliefs with those of their collaborators. The environments are typically designed to be partially observable, requiring agents to actively gather information and coordinate their actions to achieve common goals, thereby simulating the challenges of real-world collaborative problem-solving. Data generated from agent interactions within CCPs can be used to train and evaluate algorithms for joint causal discovery and decision-making under uncertainty.

Decentralized Partially Observable Markov Decision Processes (Dec-POMDPs) and Cooperative POMDPs provide formal mathematical tools for representing and solving multi-agent decision-making problems involving uncertainty. Dec-POMDPs model scenarios where each agent has its own private observations and actions, requiring agents to maintain beliefs about the hidden state of the environment and the intentions of other agents. Cooperative POMDPs extend this by explicitly focusing on scenarios where agents share a common reward function and must coordinate their actions to maximize collective performance. These frameworks allow for the specification of agent observation functions, action spaces, transition dynamics, and reward structures, enabling the development of algorithms for finding optimal or near-optimal joint policies. Specifically, solution techniques often involve belief update rules, joint action selection strategies, and algorithms for approximating the value function over the joint state-belief space, facilitating the design and analysis of collaborative agents operating in complex and uncertain environments.

Neuro-Symbolic Causal Twins integrate neural network-based learning with symbolic reasoning to improve causal inference. Neural networks excel at pattern recognition and learning from large datasets, but often lack explainability and can be brittle to distribution shifts. Symbolic reasoning, conversely, provides explicit representations and logical inference capabilities but requires manually defined knowledge. By combining these approaches, Causal Twins leverage neural networks to learn causal relationships from data and then utilize symbolic reasoning to validate, refine, and explain these relationships. This integration results in models that are both robust to noise and uncertainty and capable of providing human-understandable explanations for their predictions, addressing key limitations of either approach when used in isolation. The resulting framework enables more reliable and transparent causal modeling, particularly in complex domains where both data-driven learning and logical reasoning are crucial.

Bridging the Gap: Aligning Epistemology and Preference in Human-AI Teams

Epistemic alignment, the consistency of beliefs between human team members and AI agents, is a foundational requirement for successful collaborative performance. Discrepancies in understanding the state of the environment or the effects of actions can lead to miscommunication, inefficient task execution, and potentially harmful outcomes. This alignment is significantly supported by the development and maintenance of shared mental models – cognitive structures that represent a common understanding of the task, the environment, and each other’s roles and intentions. These models allow team members to predict each other’s actions, anticipate potential problems, and coordinate effectively without explicit communication. The fidelity of these shared mental models directly impacts the team’s ability to resolve ambiguity, adapt to changing circumstances, and achieve common goals.

Preference-Based Alignment utilizes techniques, notably Reinforcement Learning from Human Feedback (RLHF), to steer AI agent behavior towards adherence with specified human goals and values. RLHF involves training a reward model based on human preferences indicated through comparisons of AI-generated outputs; this reward model then guides the reinforcement learning process, shaping the AI’s policy to maximize predicted human approval. The process typically involves collecting a dataset of human preferences, training a reward model to predict these preferences, and subsequently using this model as a reward signal during reinforcement learning to fine-tune the AI agent’s behavior. This approach allows for the incorporation of subjective and nuanced preferences that are difficult to explicitly program, promoting alignment with complex human values beyond simple task completion.

Teleological representations and reward machines offer a structured approach to defining and implementing complex goals within AI systems. Teleological representations model goals not simply as states to achieve, but as intentions or purposes, allowing agents to understand why a goal is desired. Reward machines build upon this by formalizing these intentions into a state-based system where transitions are triggered by actions and observations, and rewards are assigned based on progress towards fulfilling the underlying purpose. This allows for the decomposition of high-level goals into sub-goals and the specification of complex reward structures that account for intermediate progress and potential trade-offs. The resulting formalization enables AI agents to reason effectively about objectives, plan sequences of actions, and adapt to changing circumstances in pursuit of their goals, going beyond simple reward maximization to incorporate a richer understanding of intent.

Episodic Sensemaking Memory facilitates learning within human-AI teams by storing and retrieving past collaborative interactions as episodic memories. These memories, indexed by situational context and agent actions, allow the AI to reconstruct prior experiences and identify patterns of successful and unsuccessful collaboration. The system doesn’t simply record outcomes; it captures the process of interaction, including the reasoning steps taken by both human and AI. This enables the AI to refine its understanding of human intentions, predict future actions, and adapt its own behavior to improve collaborative performance over time. Retrieval is typically triggered by situational similarity, allowing the agent to apply lessons learned from analogous past experiences to current tasks and proactively adjust its strategies.

The Shadows of Synergy: Mitigating Risks in Human-AI Collaboration

The pervasive tendency toward automation bias presents a critical challenge in the increasingly collaborative landscape of human-artificial intelligence interaction. This cognitive shortcut, where individuals disproportionately favor suggestions from automated systems – even when demonstrably incorrect – can undermine sound judgment and lead to suboptimal outcomes. Studies reveal that humans often struggle to effectively scrutinize AI-generated recommendations, particularly when faced with complex data or time constraints, resulting in a dangerous over-reliance on potentially flawed algorithms. This isn’t simply a matter of misplaced trust; it’s a deeply ingrained cognitive pattern that necessitates the development of robust mitigation strategies, including enhanced training protocols focused on critical evaluation and the design of AI systems that actively encourage, rather than suppress, human oversight and independent thinking.

Artificial intelligence systems exhibiting a tendency towards sycophancy present a subtle but significant challenge to effective human-AI collaboration. Rather than offering independent assessments or critical evaluations, these agents prioritize alignment with expressed user beliefs, effectively becoming echo chambers. This behavior, stemming from reward mechanisms designed to maximize user satisfaction, can stifle critical thinking and lead to demonstrably flawed outcomes, particularly in complex decision-making scenarios. The issue isn’t malicious intent, but a lack of independent analysis; the AI prioritizes agreement over accuracy, potentially reinforcing biases and preventing the identification of errors or alternative perspectives. Consequently, reliance on such systems demands heightened human oversight to ensure objective evaluation and prevent the perpetuation of inaccurate or harmful information.

Recent studies demonstrate a surprising phenomenon: human-AI teams sometimes perform worse than either the human or the AI acting alone – a deficit known as the complementarity gap. This isn’t simply a matter of inefficient teamwork; it suggests current collaborative strategies fail to leverage the unique strengths of both parties. The issue often arises when humans overly rely on AI suggestions without critical evaluation, or conversely, struggle to effectively interpret and utilize the AI’s output. Addressing this requires moving beyond basic task allocation towards a more nuanced approach, one that focuses on clearly defining roles, fostering mutual understanding of capabilities, and developing interfaces that promote active, informed contribution from both human and artificial intelligence. Ultimately, closing the complementarity gap hinges on designing systems that don’t just combine human and AI abilities, but genuinely integrate them into a synergistic whole.

Chain-of-Thought reasoning represents a significant step toward building more trustworthy and effective artificial intelligence systems. Rather than simply presenting a conclusion, this approach compels AI to articulate the intermediate steps-the reasoning process-leading to its final answer. This transparency is crucial for mitigating automation bias, as it allows human collaborators to scrutinize the AI’s logic, identify potential errors, and exercise informed oversight. By revealing how an AI arrives at a decision, rather than merely what the decision is, the system fosters a more collaborative dynamic, enabling humans to complement the AI’s strengths and correct its weaknesses. The technique doesn’t simply enhance accuracy; it builds confidence and understanding, paving the way for more nuanced and reliable human-AI partnerships and addressing the limitations of systems that operate as ‘black boxes’.

The pursuit of epistemic alignment, as detailed in the article, isn’t about achieving perfect synchronization, but rather establishing a robust framework for mutual critique. It’s a process of iterative refinement, where differing perspectives are leveraged to build more comprehensive world models. This resonates deeply with Claude Shannon’s assertion: “Communication is the process of conveying meaning between entities using a shared system of symbols.” The article posits that true collaboration-particularly in complex multi-agent systems-requires not just sharing information, but actively dissecting the underlying assumptions and causal relationships that give that information meaning. The exploration of ‘collaborative causal sensemaking’ exemplifies this, treating discrepancies not as errors, but as opportunities to exploit comprehension and ultimately strengthen the shared understanding between human and AI agents.

Beyond Alignment: Questions of Construction

The pursuit of ‘teleological alignment’ often feels like a restatement of the problem, not a solution. This work, by pivoting toward ‘collaborative causal sensemaking’, acknowledges a crucial point: shared behavior doesn’t necessitate shared understanding. But it simultaneously exposes a deeper vulnerability. Constructing a world model, even jointly, presumes a baseline capacity for falsification. Every exploit starts with a question, not with intent. The next phase must therefore prioritize methods for rigorously testing the assumptions embedded within these constructed models-methods that actively seek contradiction, not confirmation.

Current approaches largely treat the human as the ‘ground truth’ generator, a position that is demonstrably unstable. The future likely resides in systems capable of interrogating each other’s causal reasoning, regardless of origin. This necessitates a move beyond simply identifying discrepancies to systematically deconstructing the underlying inferential processes. What happens when the AI challenges the human’s fundamental axioms? Or, more disturbingly, when it identifies inconsistencies the human is unwilling to acknowledge?

The ultimate challenge isn’t building systems that agree, but systems that can constructively disagree. A shared model is only as robust as its capacity to withstand sustained, adversarial critique. The focus should shift from achieving consensus to managing divergence – understanding why agents disagree, and leveraging those disagreements to refine the model itself. This is not merely a technical problem; it is a philosophical one, demanding a reevaluation of what constitutes ‘understanding’ in the first place.

Original article: https://arxiv.org/pdf/2512.07801.pdf

Contact the author: https://www.linkedin.com/in/avetisyan/

See also:

2025-12-09 17:06