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Group Decision-Making: Common Models in Organizational Theory

Organizational theory has developed various models of decision processes to understand how decisions are made within organizations. These models analyze the structures, behaviors, and contextual factors influencing decisions, aiming to improve their quality and outcomes. This text outlines key models. It highlights their theoretical underpinnings, practical applications, and comparative insights, with a focus on synthesizing their contributions to organizational decision-making.

This text is part of the series on the design of decision governance. Decision Governance refers to values, principles, practices designed to improve the quality of decisions. Find all texts on decision governance here, including “What is Decision Governance?” here.

The Rational Model

The rational model assumes that decision-makers aim to maximize utility by selecting the optimal choice from a set of alternatives. This assumption contrasts sharply with other models, such as the bounded rationality model, which acknowledges limitations in cognitive capacity and information availability, leading individuals to satisfice rather than optimize. Similarly, the incremental model diverges from the rational model by focusing on small, iterative adjustments instead of comprehensive evaluations. Unlike the garbage can model, which emphasizes the randomness and ambiguity of decision-making, the rational model presumes a structured and goal-oriented process. These contrasts highlight the range of perspectives on how decisions are made within organizations, offering insights into their theoretical and practical implications. The process involves identifying objectives, gathering relevant information, generating alternatives, evaluating these alternatives against defined criteria, and selecting the best option. This model is grounded in economic theory, particularly the concept of rational choice (Simon, 1947).

The rational model presumes that decision-makers have access to complete information and unlimited cognitive capacity. These assumptions seldom hold true in real-world organizational settings, where constraints such as incomplete data, time limitations, and cognitive biases are prevalent. Consequently, the rational model serves as a prescriptive ideal rather than a descriptive account of actual decision-making processes.

Factors influencing decision-making in the rational model:

  • Availability of complete and accurate information
  • Decision-maker’s cognitive capacity
  • Defined objectives and evaluation criteria
  • Adequate time for analysis
  • Systematic comparison of alternatives
The Bounded Rationality Model

Herbert Simon’s concept of bounded rationality addresses the limitations of the rational model. This model recognizes that decision-makers operate under constraints such as limited information, time, and cognitive resources. Rather than seeking the optimal solution, individuals aim for a “satisficing” solution—one that meets an acceptable threshold of satisfaction (Simon, 1955).

Bounded rationality suggests that decision-making is influenced by heuristics, organizational routines, and the decision environment. It emphasizes the role of cognitive biases and structural constraints in shaping choices.

Factors influencing decision-making in the bounded rationality model:

  • Limited availability of information
  • Time constraints on decision-making
  • Use of heuristics to simplify choices
  • Cognitive limitations of decision-makers
  • Influence of organizational routines and context
The Incremental Model

The incremental model, introduced by Charles Lindblom (1959), posits that decision-making in organizations is often a process of small, sequential steps rather than comprehensive analysis. Known as “muddling through,” this model assumes that decision-makers focus on immediate, achievable improvements rather than long-term, optimal solutions. Unlike the rational model, which advocates for a systematic evaluation of all alternatives to maximize utility, incrementalism emphasizes practical constraints and the necessity of making decisions with limited time and resources. It shares a connection with the bounded rationality model in recognizing these constraints; however, while bounded rationality addresses the limitations in human cognitive capacity to process information, the incremental model focuses more on the organizational context and the iterative, feedback-driven nature of decision-making. Together, these models highlight the divergence from the idealized rational approach and the practical realities of organizational decision processes.

This approach is particularly relevant in complex and uncertain environments where comprehensive analysis is infeasible. Incrementalism emphasizes the iterative nature of decision-making and the importance of feedback loops.

Factors influencing decision-making in the incremental model:

  • Immediate resource and time constraints
  • Focus on achievable, short-term improvements
  • Feedback from previous decisions
  • Organizational resistance to radical changes
  • Complexity of the environment and decisions
The Garbage Can Model

The garbage can model, developed by Cohen, March, and Olsen (1972), describes decision-making in “organized anarchies,” where goals, technologies, and participant involvement are ambiguous. This model is structured around four independent streams: decision opportunities, participants, solutions, and problems. These streams interact randomly, creating decision outcomes when they converge at a point in time. For example, a decision might arise when a problem encounters a solution with the right participants present during a decision opportunity. An illustration of this is in a university setting, where a decision to launch a new program might result from the convergence of a faculty member’s proposal (solution), a departmental need (problem), the availability of funding (decision opportunity), and the presence of key administrators (participants).

This model challenges traditional notions of rationality and highlights the role of serendipity, timing, and organizational context in shaping decisions. It is particularly applicable to loosely coupled organizations and situations characterized by high uncertainty.

Factors influencing decision-making in the garbage can model:

  • Ambiguity in goals and technologies
  • Fluid participation of decision-makers
  • Timing and random convergence of streams
  • Availability of solutions and decision opportunities
  • Organizational context and structure
The Political Model

The political model views decision-making as a process of negotiation and power dynamics among individuals and groups with conflicting interests. Decisions emerge from bargaining, coalition-building, and compromise rather than objective analysis (Pfeffer, 1981). According to this model, decision-making is influenced by factors such as the distribution of power and resources, the interests and priorities of stakeholders, and the organizational culture that shapes interactions and strategies. These factors determine the relative influence of participants and the outcomes of negotiations, highlighting the subjective and contested nature of decision-making within organizations.

This model emphasizes the influence of power structures, resource dependencies, and organizational culture on decision outcomes. Unlike the rational model, which assumes decision-making is a logical process of optimizing outcomes based on objective criteria, the political model highlights the contested and subjective nature of decision-making, driven by negotiation and power dynamics. The rational model focuses on achieving efficiency and optimality through structured analysis, whereas the political model recognizes that decisions often result from the interplay of competing interests and the exercise of influence.

Factors influencing decision-making in the political model:

  • Distribution of power among participants
  • Conflicting stakeholder interests
  • Availability and control of resources
  • Negotiation and coalition-building processes
  • Influence of organizational culture and norms
The Contingency Model

The contingency model posits that decision-making processes depend on situational factors such as task complexity, environmental uncertainty, and organizational structure (Lawrence & Lorsch, 1967). Task complexity refers to the level of difficulty and interdependence involved in completing a task, which can dictate the type of decision-making process required. Environmental uncertainty encompasses the degree of unpredictability in external conditions, such as market volatility or technological change, which influences how decisions are approached. Organizational structure includes the formal arrangement of roles, responsibilities, and hierarchies, affecting the flow of information and authority in decision-making. This model argues that there is no single best way to make decisions; instead, the effectiveness of a decision process is contingent on these contextual variables. By analyzing various combinations of these factors, the model provides guidelines for tailoring decision processes to specific scenarios. For instance, when task complexity is high and environmental uncertainty is low, a structured and analytical approach might be optimal. Conversely, in highly uncertain environments with simpler tasks, adaptive and iterative decision-making processes may yield better outcomes. These predictions emphasize the importance of aligning decision-making strategies with the interplay of contextual variables to enhance their practical applicability.

The contingency model encourages adaptability and alignment between decision-making processes and the organizational environment, ensuring that the chosen approach is suitable for the specific circumstances faced by the organization.

Factors influencing decision-making in the contingency model:

  • Task complexity and interdependence
  • Degree of environmental uncertainty
  • Organizational structure and hierarchy
  • Availability of resources and expertise
  • Alignment between decision-making processes and context
The Behavioral Decision Theory Model

Behavioral decision theory explores the psychological and social factors influencing decision-making. It incorporates insights from cognitive psychology, such as heuristics and biases (Tversky & Kahneman, 1974), as well as the role of emotions, social norms, and group dynamics. In organizational theory, this model is used to understand how decisions deviate from rational models due to cognitive limitations and social influences. For instance, it examines how groupthink can constrain decision-making in teams, or how anchoring biases affect the evaluation of options during strategic planning. Behavioral decision theory also highlights how organizational norms and emotional responses shape individual and collective decisions, providing a framework to design interventions that mitigate biases and improve decision outcomes.

Factors influencing decision-making in the behavioral decision theory model:

  • Cognitive biases such as heuristics, anchoring, and overconfidence
  • Emotional responses influencing judgment
  • Social norms and group dynamics
  • Organizational culture and practices
  • Interventions to address biases and enhance decision accuracy By integrating these insights, organizations can better account for human behavior and develop processes that align with realistic decision-making tendencies.
The Cybernetic Model

The cybernetic model, inspired by systems theory, views decision-making as a feedback-driven process. Organizations are seen as systems that monitor their environment, compare performance against desired goals, and adjust behavior based on feedback (Ashby, 1956). More recent applications of the model in organizational theory include its use in performance management systems, where real-time data and analytics enable organizations to continuously adapt strategies. For example, digital dashboards and key performance indicators (KPIs) provide feedback loops that help organizations assess progress and recalibrate efforts to achieve desired outcomes (Beer & Eisenstat, 2000). Additionally, cybernetic principles are central to the concept of organizational resilience, where feedback mechanisms allow systems to adapt to disruptions and maintain functionality (Holling, 2001). This model emphasizes the importance of continuous monitoring, learning, and adaptation in decision processes, making it particularly relevant in dynamic and rapidly changing environments driven by technological innovation and global competition.

Factors influencing decision-making in the cybernetic model:

  • Availability and quality of feedback mechanisms
  • Real-time data and analytics capabilities
  • Alignment between performance metrics and organizational goals
  • Capacity for adaptive responses to feedback
  • Stability and resilience of organizational systems
Comparing and Synthesizing Models

These models provide complementary perspectives on decision-making. While the rational model offers a normative framework, other models focus on descriptive and explanatory aspects, accounting for organizational complexities and human limitations.

Organizations often employ elements from multiple models to design decision processes. For instance, a contingency approach may combine rational analysis with insights from bounded rationality and behavioral decision theory to address specific challenges. For example, in strategic planning, organizations might use the rational model to define clear objectives and evaluate alternatives systematically, while incorporating bounded rationality to account for information constraints and cognitive limitations. Similarly, behavioral decision theory can be applied to mitigate biases, such as overconfidence or anchoring, that may arise during critical decision-making stages. By tailoring hybrid approaches to specific contexts—such as integrating feedback loops from the cybernetic model in operational decisions or applying political model principles to navigate stakeholder negotiations—organizations can create robust, adaptive frameworks that enhance decision quality and outcomes.

Applications and Implications

Understanding these models helps organizations design decision governance frameworks that align with their context and goals. For example:

  1. Strategic Decisions: The rational and contingency models are frequently applied in strategic planning, where long-term objectives and complex variables require structured analysis.
  2. Operational Decisions: Incremental and cybernetic models are useful for operational decisions, emphasizing adaptability and real-time feedback.
  3. Crisis Management: The garbage can and political models provide insights into decision-making under high uncertainty and conflicting interests.
Conclusion

Academic research offers diverse models to analyze and improve organizational decision-making. These models highlight the interplay between rationality, context, and human behavior, providing a foundation for developing effective decision processes. Organizations can leverage these frameworks to enhance decision quality, align processes with objectives, and adapt to changing environments.

References
  • Ashby, W. R. (1956). An Introduction to Cybernetics. Chapman & Hall.
  • Beer, M., & Eisenstat, R. A. (2000). The silent killers of strategy implementation and learning. MIT Sloan Management Review, 41(4), 29-40.
  • Cohen, M. D., March, J. G., & Olsen, J. P. (1972). A garbage can model of organizational choice. Administrative Science Quarterly, 17(1), 1–25.
  • Holling, C. S. (2001). Understanding the complexity of economic, ecological, and social systems. Ecosystems, 4(5), 390–405.
  • Lawrence, P. R., & Lorsch, J. W. (1967). Organization and Environment: Managing Differentiation and Integration. Harvard Business School Press.
  • Lindblom, C. E. (1959). The science of “muddling through.” Public Administration Review, 19(2), 79–88.
  • Pfeffer, J. (1981). Power in Organizations. HarperBusiness.
  • Simon, H. A. (1947). Administrative Behavior. Macmillan.
  • Simon, H. A. (1955). A behavioral model of rational choice. The Quarterly Journal of Economics, 69(1), 99–118.
  • Tversky, A., & Kahneman, D. (1974). Judgment under uncertainty: Heuristics and biases. Science, 185(4157), 1124–1131.
Definitions
  • Bounded Rationality: A concept introduced by Herbert Simon, describing the limitations of human decision-making due to incomplete information and cognitive constraints.
  • Satisficing: A decision-making strategy that aims for a satisfactory rather than optimal solution, given practical constraints.
  • Organized Anarchy: A term from the garbage can model, referring to organizations characterized by ambiguous goals, unclear technologies, and fluid participation.