NetLogo User Community Models
WHAT IS IT?
The Garbage Can Model (GCM) of Organizational Choice (Cohen, March and Olsen 1972) is the most famous model of organizational decision-making. We previously reproduced the original Garbage Can model in an agent-based setting (Fioretti and Lomi 2008a, 2008b). With this code, we added an additional feature to the original model and, furthermore, we eliminated some unnecessary indicators.
In the GCM, decision is made when the members of an organization apply a solution to an opportunity for making a choice. Note that solutions exist before problems, and that decisions can be made even without solving any problem. Eventually, a problem may be there to be solved, but this is not necessarily the case.
In this decision process, choice opportunities take the role of "garbage cans" where solutions and problems are dumped. Hence the name of the model.
The GCM can be seen as a sort of chemical reactor where participants (decision-makers), choice opportunities, solutions and problems have been dumped. Through random meetings of these elements, decisions are made. The space where these components meet represents the organization where decisions are made.
We interpreted the GCM as an agent-based model where participants, opportunities, solutions and problems are four classes of agents. Participants are denoted by yellow men. Opportunities are denoted by orange squares. Solutions are denoted by red circles. Problems are denoted by violet triangles.
Exogenous structures may be imposed on participants, opportunities, solutions and problems:
- If "decision-structure = anarchy" participants move freely within the organization;
- If "decision-structure = hierarchy" participants and choice opportunities are ranked in order of importance and participants are only allowed to move on to opportunities of less or equal importance;
Participants are characterized by their ability in solving problems. Solutions are characterized by their efficiency. Problems are characterized by their difficulty.
Ability, efficiency and difficulty take values within a range specified by the sliders "min-ability" and "max-ability", "min-efficiency" and "max-efficiency", "min-difficulty" and "max-difficulty", respectively. They may be assigned to participants, solutions and problems according to one the following three criteria, to be selected by means of the the sliders "dist-ability", "dist-efficiency" and "dist-difficulty", respectively:
Random: The values of ability, efficiency and difficulty are assigned at random according to a uniform distribution that spans the range between minimum and maximum values;
Competence: The participants, solutions or problems with lowest identification number (conventionally, the most important participants, problems and solutions) receive the highest values of ability, efficiency or difficulty, respectively;
Incompetence: The participants, solutions or problems with lowest identification number (conventionally, the most important participants, solutions and problems) receive the lowest values of ability, efficiency or difficulty, respectively.
A problem is solved when a participant has sufficient ability and a sufficiently efficient solution such that their product is greater or equal to the difficulty of the problem. When a problem is solved, the GCM says that a decision is made "by resolution".
However, a key feature of the Garbage Can model is that a lot of decision-making may not solve any problem at all. For instance, choice opportunities may be used as showrooms in corporate politics rather than as occasions to solve problems. In these cases, decision is made "by oversight".
Decision-making by resolution takes place when at least one participant, at least one opportunity, at least one solution, at least one problem are on the same patch and the sum of the abilities of the participants on the patch, multiplied by the efficiency of the most efficient solution on the patch, is greater or equal to the sum of the difficulties of the problems on the patch. Most often, decision-making by resolution occurs when just one participant, one choice opportunity, one solution and one problem happen to be on the same patch and the ability of the participant, multiplied by the efficiency of the solution, is greater or equal to the difficulty of the problem. The patch where a decision by resolution is made takes a green color.
Decision-making by oversight takes place when at least one participant, at least one choice opportunity and at least one solution are on the same patch. No problem must be there to be solved. Thus, decision-making by oversight occurs most often when a participant, a solution and a choice opportunity happen to be on the same patch. The patch where a decision by oversight is made takes a sky-blue color.
An organization starts its life with a given number of participants, opportunities, solutions and problems. These initial values are set by means of the sliders "initial-participants", "initial-opportunities", initial-solutions" and "initial-problems", respectively. These parameters may take any integer value between 0 and 500.
Participants, opportunities, solutions and problems may be required to exit the organization after decision-making. This option is chosen by means of the switches "participants-exit?", "opportunities-exit?", "solutions-exit?" and "problems-exit?", respectively.
If the switch "participants-exit?" is ON, on each patch where a decision is made, all participants exit. If the switch "opportunities-exit?" is ON, on each patch where a decision is made one opportunity exits. If the switch "solutions-exit?" is ON, on each patch where a decision is made one solution exits: the most efficient solution if it is a decision by resolution, or a randomly chosen solution if it is a decision by oversight. If the switch "problems-exit?" is ON, all problems on the patches where a decision by resolution is made, exit.
If the switch "replace-participants?" is ON, any leaving participant is immediately replaced. The switches "replace-opportunities?", "replace-solutions?" and "replace-problems?" have a similar meaning.
If problems are on the same patch with participants, solutions and choice opportunities, but the sum of the abilities of the participants multiplied by the efficiency of the most efficient solution is less than the sum of the difficulties of the problems, all agents on the patch are blocked and no decision is made. The patch where a decision process is blocked takes a white color.
Participants can fly away from the most difficult problem involved in a blocked decision process. This is called "flight".
In the original GCM, a flight is obtained by postponing the most difficult problem. This is accomplished if an additional opportunity walks on the patch where a decision process is blocked. The most difficult problem on the patch is bound to the least important opportunity on the patch (a choice that has no effect if no hierarchical structure was imposed), and the two start walking together. The patch where they find themselves takes a brown color. The possibility of postponing decision-making is enabled by the switch "postpone?".
We added to the GCM the possibility that a flight is obtained by buck-passing. This is accomplished if only one participant is on the patch where a decision process is blocked, and an additional participant walks on it: the most difficult problem is bound to the least able among the participants on the patch, and the two start walking together. The patch where they find themselves takes a brown color. The possibility of buck-passing is enabled by the switch "buckpass?".
Once the most difficult problem left, the remaining agents may be able to make a decision, either by resolution or by oversight. Thus, flight is very important in organizational decision-making.
The switch "show-details?" allows to visualize ability, efficiency and difficulty of each agent. Furthermore, it prints an information line each time a decision is made. Each line says whether the decision was made by oversight or resolution, which agents it involved as well as the time step when they were created. If the option "show-details?" is chosen, it is advisable to enlarge the Command Center.
In previous versions, we reproduced all the indicators of the original GCM. However, our experiment highlighted that most of these indicators are redundant (Fioretti and Lomi 2008a, 2008b). At the same time, other indicators are in required in order to derive all the features of the model as emergent properties. In the end, we ended up with the following set of indicators:
- The number of participants, opportunities, solutions and problems that are in the organization at any point in time;
- The number of decisions by oversight and the number of decisions by resolution;
- The number of flights by postponement and the number of flights by buck-passing;
- The number of postponed problems in the organization, the number of passed problems in the organization, and the number of blocked decision processes;
- The number of times that a participant meets an opportunity that (s)he has already met, the number of times that a participant meets a solution that (s)he has already met, and the number of times that a participant meets a problem that (s)he has already met;
- The ratio of oversights to resolutions at high hierarchical levels, the ratio of oversights to resolutions at low hierarchical levels, and the ratio of decisions (either by oversight or by resolution) at low hierarchical levels to decisions (either by oversight or by resolution) at high hierarchical levels.
Finally, the following graphs are available:
HOW TO USE IT
The population of participants, opportunities, solutions and problems should collectively not exceed 1000 units, unless the simulation space is enlarged. Conversely, at least a few tens of each breed are needed in order to obtain interesting outcomes.
The energy of problems should not be too different to the energy of participants multiplied by the efficiency of solutions. Depending on energy configuration, maximum differences of one or two units are recommended.
With hierarchical or specialized decision structures or access structures, the number of decisions and particularly the number of decisions by resolution tends to be small. Thus, it is advisable to run the model with many opportunities, many problems, sufficiently high values of participant energy, sufficiently high values of solution efficiency and sufficiently low values of problem energy.
THINGS TO NOTICE
The most important and most robust conclusion of the Garbage Can model is that most decisions are made by oversight, few problems are solved and flight is widespread. A large number of flights do not cause decision-making. Most, but not all configurations are such that flights that cause decisions by oversight are many more than the flights causing decisions by resolution.
The energy of problems affects all the above indicators, all of which measure some aspect of the difficulty of decision-making. Energy distribution influences the outcome of decision-making as well.
Another interesting conclusion is that the most important opportunities are less likely to solve problems than the least important opportunities. This can be seen by experimenting with hierarchical decision and access structures.
THINGS TO TRY
The default values of parameters have been chosen in order to resemble the original model by Cohen, March and Olsen as closely as possible. The user is invited to move away fron default values exploring alternative combinations.
The user can try different combinations of:
EXTENDING THE MODEL
This is an agent-based version of the original Garbage Can model. A common criticism of this model is that organizational structures (decision structure, access structure) are not endogenous, which is weird for a model of organizational decision-making. In subsequent variations of the original model, we shall overcome this shortcoming.
This version runs on NetLogo 4.0.4. A previous version, also available under the rubric "User Community Models" with the name "GarbageCan", was developed on NetLogo 2.1.0. Its NetLogo 4.0.4 version is available under the rubric "User Community Models" with the name "GarbageCan_docker"
With respect to the previous version, the following changes have been made:
CREDITS AND REFERENCES
Cohen M.D., March J.G. and Olsen J.P. (1972) A Garbage Can Model of Organizational Choice. Administrative Science Quarterly, 17 (1): 1-25.
Fioretti G. and Lomi A. (2008a) The Garbage Can Model of Organizational Choice: An agent-based reconstruction. Simulation Modelling Practice and Theory, 16 (): 192-217.
Fioretti G. and Lomi A. (2008b) An Agent-Based Representation of the Garbage Can Model of Organizational Choice. Journal of Artificial Societies and Social Simulation, 11.
This model was built by:
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