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Adaptive governance is a conceptual approach that dynamically adjusts decision structures and control mechanisms to changing contexts. It combines defined accountabilities, data-informed feedback loops and iterative rules to balance agility, compliance and risk at organizational level. It supports learning governance across enterprise and domain scopes.
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What organizes, connects, or makes decisions possible.
Adaptive governance is a governance approach that organizes rules, roles, and decision paths so they can respond to new data, feedback, and changing conditions. The goal is not maximum freedom, but controlled adaptability with clear accountabilities.
The term gained prominence in climate governance, social-ecological systems, and resilience research. The practical problem was that fixed, centrally set rules often respond too slowly to new risks, local knowledge, and changing contexts in complex, multi-level systems. Adaptive governance therefore describes learning-oriented steering with feedback, participation, and rules that can be adjusted over time.
Think of adaptive governance as a control loop: observe, assess, decide, adjust. Signals from operations, stakeholders, and metrics show whether goals, risks, and responsibilities still fit. A governance forum interprets these signals, reviews guardrails and roles, and changes escalation paths or approvals when needed. In this way, learning is turned into a repeatable governance process.
Steering responds to changing conditions instead of staying fixed to one initial rule set.
Regular alignment sessions make decisions, priorities, and changes visible and binding.
Networked systems change through local interactions and cannot be planned only from the center.
Observations from practice are fed back into rules, priorities, and decisions.
Boundaries keep adaptation within agreed room for action, compliance, and risk balance.
This approach is useful when requirements, risks, or operating conditions change continuously and multiple stakeholders are involved. It is especially relevant in high uncertainty, federated organizations, or heavily regulated settings. The trade-off is coordination overhead: without good data, clear accountabilities, and a reliable decision rhythm, adaptation can become slow, inconsistent, or arbitrary.
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