The Garbage Can Model of Decision Making describes how choices occur in "organized anarchies" where goals are ambiguous, processes are unclear, and participation shifts. Decisions arise when streams of problems, solutions, participants, and choice opportunities intersect, often without a rational sequence. By recognizing the separate streams, managers can better anticipate windows where resources align with pressing issues.
Practical application involves managing attention and timing.
1972
Organizational choice under ambiguity
Explore how the model evolved over time. Click on each period to see key developments.
Initial articulation of the model highlighted how decisions emerge from the intersection of independent streams in settings like universities.
Cohen, March, & Olsen (1972) published the garbage can model to explain decision making in organized anarchies.
Conceptualization of problematic preferences, unclear technology, and fluid participation as defining features.
Computer models explored how random coupling of streams could generate seemingly rational decisions.
Subsequent work elaborated the theory and examined leadership under ambiguity in colleges and other institutions.
March & Olsen (1976) expanded the model in Ambiguity and Choice in Organizations.
Cohen & March (1986) analyzed university presidents, illustrating organized anarchies in practice.
Field research examined how budgeting and governance followed garbage can dynamics.
Researchers applied the model to government agencies, corporations, and hospitals to explain messy decision processes.
Kingdon (1995) adapted the model in his multiple streams framework for public policy agendas.
Business scholars examined how executive teams recycle solutions to fit emerging market problems.
Studies of hospital administration revealed fluid participation among physicians, nurses, and managers.
Formal modeling and public management research used the garbage can logic to simulate and evaluate policy outcomes.
Bendor, Moe, & Shotts (2001) assessed the research program and offered refined simulations.
Administrative scholars used the model to explain accountability and oversight in government agencies.
Computational approaches examined how varying participation rates affect decision timing.
Recent work links the model to digital decision analytics, crisis management, and innovation networks.
Data-rich environments allow leaders to map streams and anticipate coupling opportunities.
Government and healthcare studies use the model to analyze pandemic and disaster decisions.
Research explores how open innovation systems generate solutions seeking problems.
Organizations often operate with inconsistent or ill-defined goals, making it difficult to prioritize options.
Example: A corporate executive must balance investor expectations with environmental commitments and employee concerns.
💭 When have you had to move forward without a clear agreement on objectives?
Members may not fully understand organizational processes or how actions lead to outcomes, creating trial-and-error decision making.
Example: A hospital administrator adopts a new telehealth platform while clinicians are unsure how it integrates with existing workflows.
💭 How do you help teams act when procedures or systems are only partially understood?
Participants drift in and out of decision arenas, causing shifting coalitions and sporadic attention.
Example: A nonprofit director relies on volunteers whose availability changes week to week, altering who influences key choices.
💭 What mechanisms can you use to maintain continuity when stakeholders rotate frequently?
Issues flow independently of available solutions or decision venues, competing for limited attention.
Example: A government official confronts sudden infrastructure failures while managing ongoing policy reforms.
💭 How do you triage problems that arrive simultaneously from different parts of your organization?
Solutions may exist before problems are identified, circulating within organizations until an opportunity arises.
Example: A project manager champions a favorite software tool, waiting for a project where it can be applied.
💭 Have you observed a solution in search of a problem? What was the outcome?
Moments when decisions are expected, such as meetings or deadlines, that can couple problems and solutions.
Example: A department head uses the annual budget meeting to secure resources for a long-debated initiative.
💭 How can you create or recognize windows where your preferred solutions can meet pressing issues?
Critics argue the model explains chaos but offers limited guidance for improving decisions.
The model may underplay deliberate planning observed in many organizations.
Empirically tracking separate streams is difficult, making validation complex.
The theory fits "organized anarchies" like universities but may be less applicable to tightly controlled settings.
Some argue it underestimates how power and politics shape which problems and solutions gain attention.