Collective Intent Computing: Coordinating Multiple Human Goals in Autonomous Internet Environments

The development of intent-driven computing and autonomous Internet systems has created opportunities to transform how individuals express objectives and access heterogeneous digital capabilities. Existing intent-driven interaction frameworks, however, primarily conceptualize the user as an individual decision-maker whose intentions are interpreted and translated into executable computational tasks. In many real-world situations, Internet-based activities involve multiple people who must jointly accomplish an objective despite having different preferences, priorities, constraints, and expectations. Examples include collaborative travel planning, group purchasing, shared project coordination, collective information gathering, and multi-party service arrangements. In such situations, independently interpreting each user's intention is insufficient because the system must also determine how individual intentions can be reconciled into an acceptable collective outcome. This paper proposes Collective Intent Computing (CIC) as a conceptual extension of intent-driven computing from individual intention execution toward multi-user intention coordination. CIC conceptualizes a collective objective not as a simple aggregation of individual requests, but as an outcome of structured intent representation, preference interpretation, constraint identification, conflict analysis, negotiation, collective utility assessment, and consensus-aware execution. Its central principle is that multiple individuals should be able to express their own objectives independently while an intelligent Internet system coordinates their intentions into a jointly acceptable execution strategy. To operationalize this perspective, this paper introduces the Collective Intent Computing Framework (CICF), comprising eight interconnected components: multi-user intent acquisition, individual intent representation, collective context modeling, preference and constraint analysis, conflict detection and classification, negotiation and reconciliation, collective utility optimization, and consensus-aware execution and outcome verification. The paper further introduces a formal representation of individual and collective intentions, a conflict-aware collective objective model, and a proposed execution mechanism that connects group-level decisions with heterogeneous Internet capabilities. An important distinction of CIC is that it does not assume that collective satisfaction can always be achieved by averaging individual preferences or selecting the option preferred by the majority. Instead, it distinguishes between non-negotiable constraints, negotiable preferences, individual priorities, and group-level objectives. It also considers minority protection, preference trade-offs, participation fairness, decision transparency, and the preservation of individual intent throughout collective execution. The proposed approach may offer several potential advantages, including improved coordination of heterogeneous human objectives, reduced negotiation complexity, more transparent collective decisions, better preservation of individual requirements, cross-platform collaborative execution, and more adaptive coordination in dynamic multi-user environments. Nevertheless, these advantages are presented as theoretically motivated possibilities and research hypotheses rather than experimentally established outcomes. The paper positions Collective Intent Computing as a conceptual research direction within the broader Intent-Driven Internet perspective and identifies its theoretical foundations, architectural design, research propositions, technical challenges, and future evaluation requirements.