An Agent-Based Representation of the Garbage Can Model of Organizational Choice

Cohen, March and Olsen\'s Garbage Can Model (GCM) of organizational choice represent perhaps the first – and remains by far the most influential –agent-based representation of organizational decision processes. According to the GCM organizations are conceptualized as crossroads of time-dependent flows of four distinct classes of objects: \'participants,\' \'opportunities,\' \'solutions\' and \'problems.\' Collisions among the different objects generate events called \'decisions.\' In this paper we use NetLogo to build an explicit agent-based representation of the original GCM. We conduct a series of simulation experiments to validate and extend some of the most interesting conclusions of the GCM. We show that our representation is able to reproduce a number of properties of the original model. Yet, unlike the original model, in our representation these properties are not encoded explicitly, but emerge from general principles of the Garbage Can decision processes.Organization Theory, Garbage Can Model, Agent-Based Modelling

Passing the buck in the garbage can model of organizational choice

We reconstruct Cohen, March and Olsen's Garbage Can model of organizational choice as an agent-based model. In the original model, the members of an organization can postpone decision-making. We add another means for avoiding making decisions, that of buck-passing difficult problems to colleagues. We find that selfish individual behavior, such as postponing decision-making and buck-passing, does not necessarily imply dysfunctional consequences for the organizational level. The simulation experiments confirm and extend some of the most interesting conclusions of the Garbage Can model: Most decisions are made without solving any problem, organization members face the same old problems again and again, and the few problems that are solved are generally handled at low hierarchical levels. These findings have an implication that was overseen in the original model, namely, that top executives need not be good problem-solvers.Organizational Decision Making; Garbage Can Model; Postponing Decisions; Buck-Passing

Conflict in Organizations

{Excerpt} Michael Cohen, James March, and Johan Olsen9 have developed an influential, agent-based representation of organizational decision-making processes. They submit that organizations are—at least in part and part of the time—distinguished by three general properties: (i) problematic preferences, (ii) unclear technology, and (iii) fluid participation. Citing, “Although organizations can often be viewed conveniently as vehicles for solving well-defined problems or structures within which conflict is resolved through bargaining, they also provide sets of procedures through which participants arrive at an interpretation of what they are doing and what they have done while in the process of doing it. From this point of view, an organization is a collection of choices looking for problems, issues and feelings looking for decision situations in which they might be aired, solutions looking for issues to which they might be the answer, and decision makers looking for work.” Decision opportunities characterized by problematic preferences, unclear technology, and fluid participation, viz., ambiguous stimuli, generate three possible outcomes, each driven by the energy it requires within the confines of organizational structure. These outcomes, whose meaning changes over time, are resolution, oversight, and flight. Significantly, resolution of problems as a style for making decisions is not the most common; in its place, decision making by flight or oversight is the feature. Is it any wonder then that the relatively complicated intermeshing of elements does not enable organizations to resolve problems as often as their mandates demand

Sensitivity analysis of agent-based models: a new protocol

Agent-based models (ABMs) are increasingly used in the management sciences. Though useful, ABMs are often critiqued: it is hard to discern why they produce the results they do and whether other assumptions would yield similar results. To help researchers address such critiques, we propose a systematic approach to conducting sensitivity analyses of ABMs. Our approach deals with a feature that can complicate sensitivity analyses: most ABMs include important non-parametric elements, while most sensitivity analysis methods are designed for parametric elements only. The approach moves from charting out the elements of an ABM through identifying the goal of the sensitivity analysis to specifying a method for the analysis. We focus on four common goals of sensitivity analysis: determining whether results are robust, which elements have the greatest impact on outcomes, how elements interact to shape outcomes, and which direction outcomes move when elements change. For the first three goals, we suggest a combination of randomized finite change indices calculation through a factorial design. For direction of change, we propose a modification of individual conditional expectation (ICE) plots to account for the stochastic nature of the ABM response. We illustrate our approach using the Garbage Can Model, a classic ABM that examines how organizations make decisions

Sensitivity analysis of agent-based models: a new protocol

Agent-based models (ABMs) are increasingly used in the management sciences. Though useful, ABMs are often critiqued: it is hard to discern why they produce the results they do and whether other assumptions would yield similar results. To help researchers address such critiques, we propose a systematic approach to conducting sensitivity analyses of ABMs. Our approach deals with a feature that can complicate sensitivity analyses: most ABMs include important non-parametric elements, while most sensitivity analysis methods are designed for parametric elements only. The approach moves from charting out the elements of an ABM through identifying the goal of the sensitivity analysis to specifying a method for the analysis. We focus on four common goals of sensitivity analysis: determining whether results are robust, which elements have the greatest impact on outcomes, how elements interact to shape outcomes, and which direction outcomes move when elements change. For the first three goals, we suggest a combination of randomized finite change indices calculation through a factorial design. For direction of change, we propose a modification of individual conditional expectation (ICE) plots to account for the stochastic nature of the ABM response. We illustrate our approach using the Garbage Can Model, a classic ABM that examines how organizations make decisions

Decision Taking versus Action Determination

Decision taking is discussed in the context of the role it may play for various types of agents, and it is contrasted with action determination. Some remarks are made about the role of decision taking and action determination in the ongoing debate concerning the reverse polder development of the hertogin Hedwige polder

Modeling Routines and Organizational Learning. A Discussion of the State-of-the-Art

This paper presents a critical overview of some recent attempts at building formal models of organizations as information-processing and problem-solving entities. We distinguish between two classes of models according to the different objects of analysis. The first class includes models mainly addressing information processing and learning and analyzes the relations between the structure of information flows, learning patterns, and organizational performances. The second class focuses on the relationship between the division of cognitive labor and search processes in some problem-solving space, addressing more directly the notion of organizations as repositories of problem-solving knowledge. Here the objects of analysis are the problem-solving procedures which the organization embodies. The results begin to highlight important comparative properties regarding the impact on problem-solving efficiency and learning of different forms of hierarchical governance, the dangers of lock-in associated with specific forms of adaptive learning, the relative role of “online” vs. “offline” learning, the impact of the “cognitive maps” which organizations embody, the possible trade-offs between accuracy and speed of convergence associated with different “decomposition schemes”. We argue that these are important formal tools towards the development of a comparative institutional analysis addressing the distinct properties of different forms of organization and accumulation of knowledge.Division of labor, Mental models, Problem-solving, Problem decomposition.

Learning in evolutionary environments

Not availabl

Learning in Evolutionary Environments

The purpose of this work is to present a sort of short selective guide to an enormous and diverse literature on learning processes in economics. We argue that learning is an ubiquitous characteristic of most economic and social systems but it acquires even greater importance in explicitly evolutionary environments where: a) heterogeneous agents systematically display various forms of "bounded rationality"; b) there is a persistent appearance of novelties, both as exogenous shocks and as the result of technological, behavioural and organisational innovations by the agents themselves; c) markets (and other interaction arrangements) perform as selection mechanisms; d) aggregate regularities are primarily emergent properties stemming from out-of-equilibrium interactions. We present, by means of examples, the most important classes of learning models, trying to show their links and differences, and setting them against a sort of ideal framework of "what one would like to understand about learning...". We put a signifiphasis on learning models in their bare-bone formal structure, but we also refer to the (generally richer) non-formal theorising about the same objects. This allows us to provide an easier mapping of a wide and largely unexplored research agenda.Learning, Evolutionary Environments, Economic Theory, Rationality