21coe, university of tokyo mmrc discussion paper no....

MMRC-F-97

Analysis of organizational processes using multi-agent simulator:

Reexamination of the garbage can model

Graduate School of Economics, University of Tokyo

Nobuyuki INAMIZU

September 2006

21COE, University of Tokyo MMRC Discussion Paper No. 97

mailto:[email protected]

http://www.ut-mmrc.jp/

21COE, University of Tokyo MMRC Discussion Paper No. 97

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Analysis of organizational processes using

multi-agent simulator:

Reexamination of the garbage can model

Graduate School of Economics, University of Tokyo

Nobuyuki Inamizu

September 2006

Nobuyuki Inamizu

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Abstract This paper rebuilds the garbage can model with a multi-agent simulator, making its simulation process

visible, and reexamines the previous studies. The results of this paper are different from what the previous

studies have assumed. Under unsegmented structure, decision-makers (and occasionally problems) move

together and their combinations do not change. In addition, it is under hierarchical structure that decisions are

made depending on timing. In this way, this paper shows that visualization is a convenient tool for confirming

the simulation process.

This paper also suggests that the results of simulation can widely capture how people behave in an

organization. While demonstrating the simulation process to a manager of a firm, we realized that

decision-makers’ (and problems’) behavior under “unsegmented structure” could be observed in the firm. In

this way, this paper shows that visualization is a strong tool for empirical studies since it promotes the

conversations with managers or staff.

Key words

Multi-agent simulator, Garbage can model, visualization

1. Introduction

Many simulation models have been proposed since the birth of computers. Moreover, in social science,

many studies have built simulation models and have examined individual behaviors in organizations or groups

(e.g., Carley, 1991; Harrison & Carroll, 1991; March, 1991). Previously, in the 20th century, most of these

studies have analyzed only the numerical results of simulation. However, as discussed in the second section, we

might misinterpret these results unless the simulation process thoroughly checked.

The studies on the garbage can model, a famous simulation model on organizational decision-making,

commit the same error. Cohen, March & Olsen (1972) examine only the numerical results of simulation and

draw conclusions only from this examination. Then, as shown in the third section, the subsequent studies have

developed their arguments without precise understanding of the simulation results. This has been clearly

pointed out by Bendor, Moe & Shotts (2001). They also state that the simulation model of Cohen et al. (1972)

(CMOSM) has many defects and that this model should be discarded in order to resume the studies using the

garbage can model. This criticism has great significance because the garbage can model is widely known.

Therefore, we will reexamine CMOSM in this paper, following Bendor et al. (2001).

Analysis of organizational processes using multi-agent simulator

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First, we will rebuild CMOSM, making its simulation process visible. Next, we will meticulously confirm

the simulation process. Then, we will apply this result to actual organizations and stimulate discussions. When

rebuilding CMOSM, we will use a multi-agent simulator, KK-MAS1, which is excellent for drawing the

simulation process.

Previous studies assume that, under unsegmented structure, combination of problems, decision-makers

and solutions changes, and decisions are made depending on timing. However, the results of this paper are

different. Under unsegmented structure, decision-makers (and occasionally problems) move together, and their

combinations do not change. In addition, it is under hierarchical structure that decisions are made depending on

timing.

This paper also suggests that these results are observed in actual organizations. When demonstrating the

simulation process to a manager of a firm, we realized that decision-makers’ (and problems’) behaviors under

unsegmented structure could be observed in the firm. When reviewing a case study by March & Romelaer

(1976), we realized that decisions depending on timing under hierarchical structure might be observed in actual

organizations.

Moreover, this paper shows two advantages of making the simulation process visible. First, it is a

convenient tool for confirming the simulation’s numerical results and their implications. Second, when we

apply the results to an actual organization, it proves to be a strong tool promoting conversations with managers

or staff.

Through these analyses, we will show the directionality to the studies on the garbage can model and to the

studies using computer simulations on organizations or groups.

2. The existing studies using simulation: the importance of the simulation process

Currently, studies on organizational behavior using computer simulation are growing2. A feature of

organizational behaviors is that even if rules at the micro level (individual behavior) are simple, results at the

macro level (organizational behaviors) are hardly imaginable. Therefore, the studies involve computer

simulation (Cohen et al., 1972; Carley, 1991; Harrison & Carroll, 1991; March, 1991).

Many previous studies using computer simulation have only analyzed the numerical results. However, is it

1 KK-MAS is a simulator to run multi-agent simulation and was developed by Kozo Keikaku Engineering Inc (http://www.kke.co.jp/index.html). 2 Carley (1995) surveys existing studies on a mathematical model or computer simulation of organizations. “Computational & Mathematical Organization Theory” was launched in 1995, and many studies using computer simulation have been published. In addition, textbooks on computer simulation have been published (e.g., Gilbert & Troitzsch, 1999).

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enough to analyze only the numerical results of the simulation without checking its process where rules affect

intricately? Below, we will consider March (1991) and Takahashi (1998) and point out the danger of analyzing

only the numerical results.

March (1991) is a famous paper on organizational learning using computer simulation. He simulated a

process where individual knowledge and organizational knowledge gained from each other and adapted to their

environment. He defined “equilibrium” as the state where all individual knowledge and organizational

knowledge are the same and do not change. From the numerical results of simulation, he concludes that when

the rate at which individual knowledge learns from organizational knowledge is low, and the opposite is high,

they both adapt well to their environment.

However, Takahashi (1998) examined the model of March (1991), scrutinizing its stimulatory process, and

pointed out that March (1991) errs in interpreting the simulation results. Takahashi (1998) reported that

nonequilibrium lock-in (falling into the state where individual knowledge and organizational knowledge do not

change even though they are not the same) often can be observed. According to him, March (1991) might

include this nonequilibrium in the equilibrium and reach the conclusion mentioned above. Takahashi (1998)

then concludes that when the learning rate of individual knowledge is high and the learning rate of

organizational knowledge is low, they both adapt well to their environment. This is contradictory to the

conclusion of March (1991).

Therefore, analyzing only the numerical results of the simulation might draw a false conclusion. Although

it is natural to check the simulation process thoroughly, to emphasize this again has significance.

3. The garbage can model and development of the research Cohen et al. (1972), the original paper on the garbage-can model, is cited 873 times3, and this frequency is

numorous among social science papers on using computer simulation. The field citing the paper extends from

business administration to politics or sociology. In addition, the garbage can model has been mentioned in

standard textbooks on organization theory (e.g., Daft, 2004; Scott, 2003; Hatch, 1997; Miyakawa, 2002). In

2001, however, the model faced criticism from Bendor and his colleagues (Bendor et al., 2001). Since the

garbage can model is widely known, this criticism has great significance. Below, let us see what the garbage

can model is, the background of the criticism of Bendor et al. (2001) as well as the criticism itself.

3.1. The simulation model proposed by Cohen et al. (1972)

Let us describe concisely the background of Cohen et al. (1972). Classical theories of choice in

3 The retrieval result from web of science on June 3, 2006.


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organizations have three assumptions: 1) decision-makers know all outcomes of all alternatives, 2) they share

the same goal, and 3) they have consistent and precise preferences. Theories of bounded rationality relaxed the

first assumption, and theories of conflict relaxed the second. Most theories of organizational choice, however,

do not relax the third assumption. One of the few theories relaxing it is the garbage can model (March & Olsen,

1986).

Cohen et al. (1972) model decision-making in organized anarchies. Organized anarchies are organizations

characterized by problematic preferences, unclear technology, and fluid participation. In this circumstance, an

organization is a collection of choices looking for problems, issues 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. A decision is an outcome of the complex interactions of these four independent streams within an

organization. Cohen et al. (1972) considered that participants take part in decisions with changing (inconsistent

and imprecise) preferences and make decisions by timing of choices, problems, and participant availability.

Since such a model of organizational decision-making must concern itself with a complicated interplay

among problems, solutions, decision-makers, and choice opportunities, it is difficult to build a mathematical

model. Then, Cohen et al. (1972) translated the situations of decision-making into a computer-simulation model

and sees how decisions are made.

The image of the simulation model is as follows: Choice opportunities are viewed as garbage cans in the

model. Various kinds of problems, energies, and solutions are dumped by participants into choice opportunities,

as if they threw garbage into cans. When the total energy in a choice opportunity exceeds the requirements to

solve the problems there, the choice is made, as if a full garbage can were cleared out (Kuwashima &

Takahashi, 2001). With this image in mind, let us move on to the details of the model (also see Figure 1).

The model has five fixed parameters: 1) number of time periods, 20; 2) number of choice opportunities,

10; 3) number of decision-makers, 10; 4) number of problems, 20; and 5) the solution coefficients for the 20

time periods, 0.6 for each period. The solution coefficient defines the effective energy devoted by

decision-makers (discussed later).

The model has eight variables (see Table 1). The 324 cases were obtained by considering the possible

combinations of the six variables except energy requirement (ER) and effective energy (EE). For each case,

simulations are conducted obeying the following rule:

1. Choice opportunities and problems enter following ec and ep4.

4 A choice opportunity and two problems enter per time period over the first 10-time periods.

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Figure 1: The flow chart of CMOSM

＋ 0 or －

No

Yes

Yes

No

No

Yes

Read fixed parameters：Preiods(20), Choice opportunies(10)Problems(20), Decision-makers(10),

Solution coefficient(0.6)

Start

Initial setting of Choiceopportunities1) Entry time(ec)、2) Energy Requirement(ER)=1.1

Set one of the combinationsof variables (*1)

Set as T=1 ("T" represents periods).

Enter a Choice opportunity and two problems,followeing "ec" and "ep." (*2)

Allocate1) Decision-makers2) Unsolved problemsto Choice opportunities not made, following"A," "D" and "allocation assumption (*3)."

Calculate total of "ER" in eachChoice opportunity, based on "L."

Calculate total of "EE" in each Choice opportunity,based on "E" and Solution coefficient.

ER-EE

The Choice opporutunity is notmade, and the problems thereare not solved. (The Choiceopportunity holds deficit ofenergy (ER-EE). )

The Choice opportunity ismade, and Problems there aresolved.

Did you execute about allChoice opportunities？

T=T+1

T <= 20

Change the combination of variables

Did you execute about allpossible combinations?

Exit

Initial setting of Problems1) Entry time(ep)、2) Access-structure(A)3) Load(L)

Initial setting of Decision-makers1) Decision-structure(D)2) Energy distribution(E)

Enta (1987) revised by the author

*1 The number of all possible combinations is 324 because ec and ep have two types and A, L, D, and E have 3

types respectively.

*2 All choice opportunities and problems enter over the first 10 periods.

*3 The assumptions allocate problems and decision-makers to the choice opportunity closest to a decision.


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Table 1: The variables of CMOSM Variable Definition Energy Requirement (ER) The energy required to make a choice:

The initial value = 1.1 Effective Energy (EE) The sum of the energies which decision-makers have devoted, deflated

by the solution coefficient: The initial value = 0.0

Entry time of choice opportunity (ec)

The calendar time at which that choice opportunity is activated. Two types: * One choice opportunity enters per time period over the first 10 time

periods. Entry time of problems (ep) The calendar time at which that problem is activated.

Two types: * Two problems enter per time period over the first 10 time periods.

Access-structure (A) A list of choice opportunities to which the problem has access. Three types:

1) Unsegmented: each problem has access to all choice opportunities.

2) Specialized: each problem has access to only one choice opportunity.

3) Hierarchical: the number of choice opportunities to which each problem has access differs.

Load (L) The energy required to solve a problem. Three types: 1) light, 2) moderate, and 3) heavy.

Decision-structure (D) A list of choice opportunities to which the decision-maker has access. Three types:

1) Unsegmented: each decision-maker has access to all choice opportunities.

2) Specialized: each decision-maker has access to only one choice opportunity.

3) Hierarchical: the number of choice opportunities to which each decision-maker has access differs.

Energy Distribution (E) The distribution deciding how much energy each decision-maker has. Three types: 1) equal energy, 2) important people more energy, and 3) important people less energy.

2. Decision-makers and unresolved problems are attached to choice opportunities not made, following A, D,

and allocation assumptions. The assumptions allocate problems and decision-makers to the choice opportunity

closest to a decision (EE-ER is minimized).

3. ER of each choice opportunity is obtained based on L.

4. EE of each choice opportunity is obtained based on E and the solution coefficient. To be specific, it is the

sum of the energies devoted by decision-makers to choice opportunity, deflated by the solution coefficient 0.6.

5. If ER-EE is zero or below, the choice opportunity is decided, and the problems are resolved. However, if

ER-EE is more than zero, it becomes opposite.

The above rule induces three types of decisions. If the choice opportunity having problems (ER > 0) is

Nobuyuki Inamizu

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decided, the decision is based on the resolution. The decisions are also made even if the choice opportunity has

no problems. Cohen et al. (1972) consider two types of these decisions. The first is “decision by oversight,”

which is made before decision-makers enter the choice opportunity. The second is “decision by flight,” which

is made after all problems in the choice opportunity leave there.

3.2. The results of the simulation and their interpretation

Cohen et al. (1972) drew eight implications from the numerical measurements. (e.g., problem persistence,

decision-maker velocity, decision difficulty5). For example, “An increase in the net energy load on the system

generally increases problem activity, decision maker activity, decision difficulty, and the uses of flight and

oversight6 . . . As load changes, an organization that has an unsegmented access structure with a specialized

decision structure stays quite stable. It exhibits relatively low decision difficulty and decision maker activity,

very low problem latency, and maximum problem activity7. It makes virtually all decisions placed before it,

uses little energy from decision makers, and solves virtually no problems (p. 9).”

From these descriptions, however, we can hardly imagine how the decision-making process advances. A

reason for this is that, although the model assumes problems, solutions, decision-makers, and choice

opportunities flow through an organization, we cannot identify how they flow only from the numerical

measures. In such a case, we may also misinterpret the results of simulation.

In fact, when reviewing subsequent studies by the original authors, we realized that they were unclear of

the results from the model. For example, Cohen et al. (1972) stated that decision by resolution is the most

uncommon style for the conditions where problems are restricted in movement. March & Olsen (1986),

however, stated that it is under the conditions where problems and decision-makers are restricted in movement.

In addition, March (1994) stated that it is under the conditions where problems, decision-makers, and solutions

are restricted in movement. First, they argued only on the access-structure of problems. Next, they included the

decision structure of decision-makers in the discussion. Finally, they referred to the movement of solutions not

explicitly incorporated in the model. Moreover, they have not reexamined CMOSM during this period8.

5 Problem persistence is the total number of time periods a problem is activated and attached to a choice, summed over all problems. Decision-maker velocity is the total number of times any decision-maker shifts from one choice to another. Choice persistence is the total number of time periods during which a choice is activated, summed over all choices. 6 Decisions are made not only by resolution but also by oversight or flight, which means that decisions are made depending on timing. 7 Problem latency is the total number of time periods during which a problem is activated, but not attached to a choice, summed over all problems. Problem velocity is the total number of times any problem shifts from one choice to another. 8 There are few studies on the garbage-can model using simulation. We can find only Anderson & Fischer (1986) and Takahashi (1997).


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Therefore, we cannot help considering that, the original authors have little understanding of the results of

CMOSM and have misinterpreted them.

Eventually, the eight implications from CMOSM, including misinterpretation, have been summarized in

only one sentence. Under unsegmented structures (problems, decision-makers, and solutions can move from

choice to choice freely), the combinations of problems, decision-makers, solutions, and choice opportunities

shift, and decisions are made depending on timing.

3.3. Development of relevant studies on the garbage-can model

Many researchers have developed arguments based on the summarized implication. March & Romelaer

(1976) consider the features of university decision-making as the garbage-can process, and they compare that

process to a metaphor: a strange soccer game9. This metaphor describes shifting combinations of problems or

decision-makers under unsegmented structure. Grandori (1984) considers that the garbage-can model insists

decisions are a function of timing under unsegmented structure. Padgett (1980) attempts to accommodate the

garbage-can model to hierarchical organizations, and this idea itself shows that many studies assume the

garbage-can process can be observed under unsegmented structure.

Since the implications have been summarized, many studies only describe decision-making processes by

the garbage-can metaphor and do not examine the results of CMOSM (e.g., March & Olsen 1976). Therefore,

most of these studies do not correspond to CMOSM. Kingdon (1984) does not pursue the result of CMOSM,

although his concept of “Policy Window Model” is suggested by the garbage-can model. Although the standard

textbooks on organization theory introduce the garbage-can model, they do not refer to the CMOSM results

(Daft, 2004; Scott, 2003; Hatch, 1997; Miyakawa, 2002). Martin (1981), discussing the process of research on

psychology, considers the garbage-can model as a metaphor rather than a systematic theory.

3.4. Criticism from Bendor et al. (2001)

We have noted that the original authors and many other researchers have misinterpreted the results of

CMOSM and that they have developed their arguments using the garbage-can model as a metaphor. Bendor et

al. (2001) have pointed this out more clearly.

Bendor et al. (2001) show that the results of CMOSM have been misinterpreted. They note that

garbage-can theory insists on decision-making in “organized anarchy” depends on timing, and that CMOSM

9 “Consider a round, sloped, multi-goal soccer field on which individuals play soccer. Many different people (but not everyone) can join the game (or leave it) at different times. Some people can throw balls into the game or remove them. Individuals while they are in the game try to kick whatever ball comes near them in the direction of goals they like and away from goals they wish to avoid.” (p. 276)

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should have modeled this theory in a computer laboratory. However, as they rebuilt CMOSM, they observed

that the decision-makers (and occasionally problems) move together from choice to choice under unsegmented

access- and decision-structure. This means that the combinations of problems, decision-makers, and choice

opportunities do not change under a typical condition in the garbage-can process. This finding contradicts the

garbage can theory, which CMOSM should have been based on. Then, Bendor et al. (2001) pointed out that the

reason for this result was the inappropriate rules.

Bendor et al. (2001) showed a possible path that the studies on the garbage-can model should follow.

According to them, we should have distinguished the garbage can theory from CMOSM, and should have

replaced it. As a result, the garbage-can theory and CMOSM have mixed, and they have stooped to a metaphor

to describe the decision-making. Therefore, we should discard CMOSM and make a new model based on the

garbage can theory.

To summarize, the existing studies use the garbage-can model as a metaphor without precise

understanding of CMOSM. A probable reason for this would be the analysis of only the numerical results.

In this paper, we will reexamine the CMOSM results, checking its simulation process. Although Bendor et

al. (2001) reported the CMOSM process, they did not refer to the results, except under unsegmented access-

and decision-structure. Therefore, we are not sufficiently aware of the results under hierarchical or specialized

structures.

In addition, we will examine whether the results of CMOSM can be seen in actual organizations. Although

Bendor et al. (2001) assert that CMOSM is not worth studying, can we really declare this. Indeed, we have to

admit that CMOSM has some defects in its rule when reasoning deductively. However, as shown in the

following, we realized by examining the simulation process that CMOSM captures how people behave in

actual organizations. When reasoning inductively, we cannot assume that CMOSM has no significance.

4. CMOSM rebuilt with KK-MAS As the source code is given in the appendix of Cohen et al. (1972), we can rebuild CMOSM precisely. In

this section, we will rebuilt CMOSM and show that it produces almost the same numerical results of Cohen et

al. (1972). When rebuilding, we have used a multi-agent simulator, KK-MAS, which is excellent for drawing

simulation processes.

4.1. Rebuilt CMOSM and its simulation process on a computer screen

We will describe the rebuilt CMOSM with KK-MAS. As shown in Figure 2, the main window consists of


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Simulation Window (displayed as “Garbage Can Model” in Figure 2), Data Window, and Control Panel.

Figure 2: The computer screen of CMOSM

We will first explain the Simulation Window: Each part enclosed with the frame (consisting of 7 × 7 cells)

represents an individual choice opportunity. Each choice opportunity has five conditions: 1) not entered, 2)

entered but not made, 3) made by resolution, 4) made by oversight, and 5) made by flight. Each choice

opportunity on the computer screen changes its color according to its own condition. Therefore, we can

immediately understand the simulated decision-making process. Circles in choice opportunities represent

problems, and the number in a circle means the identification number. Triangles in choice opportunities

represent decision-makers, and the number in a triangle means the identification number. With these

descriptions, we can immediately see which problems or decision-makers are in which choice opportunities. In

addition, X signs are records of which problems or decision-makers were in the choice opportunity when the

choice was made. With this description, we can see how many problems are solved and how many

decision-makers have involved in the choice. Solutions are not expressed visually since they are fixed

Simulation Window

Control Panel

Data Window

This part enclosed with the frame is a choice opportunity （ It changes its color according to its condition）. Circles are problems and triangles are decision-makers.

The numbers in each circle and triangle are identification numbers.

X signs in the choice opportunities already made shows which problems or decision-makers were there when

the choice was made.

Nobuyuki Inamizu

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parameters in CMOSM.

Data Window shows various numerical results (e.g., problem activity). Control Panel enables us to set

variables easily. We can set L, AS, and DS simply by moving the slide bar in the Control Panel.

4.2. Result of precise reconstruction of CMOSM

Were we able to rebuild CMOSM with KK-MAS precisely? We examined this using Problem Activity,

Decision-Maker Activity, and Decision Difficulty. We used these measures because 1) Cohen et al. (1972) have

described the computational algorithms of these measures in the source code, and 2) they have accurately

reported the results of these measures. The value in the upper row in each cell in Table 2 is the one Cohen et al.

(1972) report, and the value in the middle row in each cell is the result of CMOSM rebuilt with KK-MAS.

Although some differences can be seen in detail, almost the same result is obtained10. Therefore, we can

conclude that CMOSM was rebuilt precisely.

10 Normally, the numerical results are the same, because the same random numbers are used. The differences of the results might be caused from a rounding error. The numerical value of KK-MAS in the table is a value after correcting the rounding error. In addition, we rebuilt CMOSM with FORTRAN and Microsoft Excel, and obtained the same results as the KK-MAS version.

Table 2: The numerical results of CMOSM Effects of Load

Problem activity

Decision-maker activity

Decision difficulty

Light 114.9 114.8 109.1

60.9 61.0 62.0

19.5 19.5 18.3

Moderate 204.3 201.3 192.5

63.8 66.0 78.2

32.9 34.1 42.6

Load

Heavy 211.1 210.0 225.3

76.6 76.9 65.7

46.1 46.1 36.5

* The value in the upper row in each cell in Table 2 is the one Cohen et al. (1972) report.

* The value in the middle row in each cell is the result of CMOSM rebuilt with KK-MAS.

* The value in the lower row in each cell is the result of CMOSM after correcting the initial condition of

choice opportunities.


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4.3. Results of correcting initial setting of choice opportunities

When we rebuilt CMOSM and examined the source code, we realized a strange setting. The initial value

of Energy Requirement (ER) of choice opportunities was set at 1.1 (see Table 1) as if there were problems in

the choice opportunity from the start. Then, we corrected the value from 1.1 to 0.0. The results of this are in the

lower row in each cell in Table 2. Although great differences can be seen at some points, we will consider this

as the results of CMOSM, KK-MAS version.

We have observed that CMOSM can be rebuilt correctly with KK-MAS, making the simulation process

visible. Below, we will reexamine studies on the garbage can model with the visible simulation process.

5. The reexamination of studies on the garbage can model 5.1. The simulation process under unsegmented access- and decision-structures

First, we will show the simulation process under unsegmented access- and decision-structure, because this

is considered a typical condition of the garbage can process.

Figure 3 shows the simulation process where load is light and access- and decision-structure are

unsegmented. From this figure, we can see decision-makers gathering and solving problems immediately.

Figure 4 shows the simulation process under a heavy load. Here, we can see that problems and decision-makers

move together from choice to choice, problems are not resolved, and decisions are made only by flight. In this

manner, Figures 3 and 4 show us the simulation process that we cannot visualize from the numerical results of

Cohen et al. (1972). The visible simulation process enables us to see intuitively and immediately what happens

during the simulation process.

As Bendor et al. (2001) point out, Figures 3 and 4 reaffirm that CMOSM does not produce the results the

garbage can theory assumes. The combinations of choice opportunities, problems, and decision-makers do not

change. The same combination of decision-makers or problems makes decisions11.

Bendor et al. (2001) conclude that these results are from the inappropriate rules of CMOSM and that these

results do not provide any suggestions for considering the decision-making in organized anarchy. However, can

really CMOSM neither provide any suggestions for studying actual organizations nor produce the results the

garbage can theory assumes? In the following section, we will address these questions using the visible

simulation process.

11 Although we revised the initial condition of choice opportunities in the previous section, this does not affect the result that the decision-makers or problems move together from choice to choice. The same result can be obtained before the revision.

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Figure 3: The simulation process under unsegmented structure and light load

* Sites enclosed by solid lines are choice opportunities respectively. Shaded sites are the ones already made. * ●: Unsolved problems, ▲: Decision-makers,

※: Solved problems, ×: Records of decision-makers having been involved in the choice. * The above figure shows the appearance at the second period. Choice opportunities are decided (by resolution) as soon as they enter. Problems are also solved as soon as they enter.

* The above figure shows the appearance at the third period. Decision-makers gather at the choice opportunity to have just entered.

* As 20 periods have passed, all choice opportunities have been decided by resolution.


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Figure 4: The simulation process under unsegmented structure and heavy load


(※: Solved problems, ×: Records of decision-makers having been involved in the choice.) * The above figure shows the appearance at the second period. Decision-makers and problems move together from choice to choice. The choice opportunity from which decision-makers and problems leave is decided by flight.

* The above figure shows the appearance at the third period. As mentioned above, decision-makers and problems move together.

* As 20 periods have passed, most choice opportunities have been decided by flight.

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5.2. An application of the results under unsegmented structure to an actual

organization

We can hardly imagine what the results under unsegmented structure imply. However, we realized that the

results can explain the case of an office reform by demonstrating CMOSM to an executive being in charge of

the reform.

5.2.1. The details of the office reform

Company X reformed its office from 2000 to 2001. The improvement had three points: 1) open-plan

setting (removing high partitions), 2) free-address setting (replacing assigned desks by freely available ones),

and 3) computerized database (enabling to monitor most of the business documents on the intranet).

We can see the whole office, although its length is as much as 80 m, because small plants and plant pots

are used instead of partitions, which obstruct the line of sight. As the office is spacious, we can see at a glance

all the employees, and we can feel the atmosphere of the entire office.

A typical feature of the office is free-address setting, allowing the employees, including the department

heads or section chiefs, to work anywhere at any time. The employees take their notebook computer, look for

an empty place, and start working. At the end of the day, they clear their desks and leave for home.

Unless the employees can carry their belongings and gain information for business anywhere, they cannot

change their location daily. First, the executive thoroughly scanned the document to solve this problem. The

employees could reduce their belongings by this thorough computerization. Second, the executive made each

employee have an individual homepage and opened it on the intranet. The individual homepage contains the

owner’s detailed self-introduction, as well as business documents and materials. The self-introduction consists

of qualifications, favorite jobs, and records of the projects he or she has carried out. As a result, the employees

were able to acquire computerized documents or information anywhere through each employee’s homepage or

intranet when necessary. In this manner, the employees were able to move freely in the office.

5.2.2. Demonstration of the simulation process and interpretation of the office reform

The results of CMOSM under unsegmented structure were surprising. Therefore, we wondered whether

the results are observed in actual organizations, and if so, what they suggest.

Since we had the opportunity to meet the executive of Company X, we performed a demonstration of the

simulation process12. We explained the garbage can model and CMOSM rebuilt with KK-MAS, and then

showed the simulation process where the load was light and access- and decision-structure were unsegmented.

12 At the office of Company X on June 2, 2004.


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After the demonstration, we asked the executive whether this process and the results could be seen in the office.

He said that a movement of the employees similar to that of the simulation might be seen in the office. Then,

guiding us around the office, he began to tell the change caused by the improvement.

We assumed that the employees at this company sat randomly in the office. However, the executive said

that the employees work near the person with whom they want to consult and that the members of the same

project sit together during discussions. In fact, we could observe that an employee sat next to his senior to learn

how to work, and another sat with the members involved in the same project. In addition, before the

improvement, the employees had to reserve the conference room and to wait in order to have a meeting. After

the reform, they have to only look for vacant spaces in the office for meeting. In fact, we could hear from an

employee that he was now able to have a business meeting without reserving the conference room.

We can explain this by interpreting the assumptions of CMOSM as follows: We can consider seats or

conference rooms as choice opportunities, and employees as problems or decision-makers (see Figure 5). We

can also consider the access- and decision-structure as a structure deciding which seats or conference rooms are

accessible to each employee13. Although these interpretations are not equal to the assumptions of CMOSM

logically, they can explain well the case of Company X.

It could be considered that access- and decision-structure were almost specialized before the office reform

because each seat (choice opportunity except conference rooms) was assigned to each employee (problem or

decision-maker). In such a case, as the employees could not use other vacant space, the members engaged in

related business had to wait for the conference room to become vacant before they could arrange even a casual

meeting. That is, they had to wait for the choice opportunity all members could access to become activated (the

upper chart of Figure 5).

It could be considered that access- and decision-structure became unsegmented after the office reform

because all the employees were enabled to choose any seats freely. In such a circumstance, if there are vacant

seats or space, they can gather there and discuss immediately. They do not need to wait for the specific choice

opportunity (the conference room) to become activated (the lower chart of Figure 5).

The employees not only came to sit near each other, but also were able to move toward solving a problem

instantly. Even if they face some problems, they can add modifications to proposals or projects on the spot by

seeing the personal computer screen because documents are computerized. Even if they required some

documents urgently, they did not have to examine and report later because they can immediately extract

13 Although we have to divide access-structure and decision-structure normally, we put them together in a structure deciding which space each employee can occupy. The employees not only devote their energies to dealing with their task, but also report problems to the others. Therefore, we can consider the employees as carriers of problems or energies. Weiner (1976) also adopts the same view.

Nobuyuki Inamizu

18

information through the intranet. Moreover, even if they had something that they were unable to understand,

they could search for and contact those versed in the problem, immediately, through each employee’s

homepage.

In all, the members were able to gather and move toward solving problems in the reformed office. This

corresponds to the results of CMOSM where load is light and access- and decision-structure are unsegmented.

Figure 5: An interpretation of the office reform of Company X

Employees (carriers of problems or energies)

Before the office reform: * Each could use only one preassigned seat. * To have a meeting, they had to gather at the conference room. However, they had to wait for the room to become vacant.

Seats (choice opportunities)

Conference room (a specific choice opportunity)

A structure deciding which space each employee

can occupy

After the office reform: * Each can use all vacant seats. * To have a meeting, they only have to find a vacant space. Therefore, they can gather immediately.

A structure deciding which space each employee

can occupy

Employees (carriers of problems or energies)

Conference room (a specific choice opportunity)

Seats (choice opportunities)


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5.3. The condition of decision-making depending on timing and some implications

Can the results of CMOSM show that decision-making is dependent on timing that the garbage can theory

assumes?

When examining the visible simulation process, we realized CMOSM produces this result under

hierarchical access- and decision-structure14. Figure 6 shows the simulation process where load is moderate,

and access- and decision-structure are hierarchical. In this figure, we can observe combinations of problems

and decision-makers are shifting, and decisions are made sometimes by resolution, sometimes by oversight or

flight. This means that decisions are dependent on timing.

Why do hierarchical access- and decision-structure produce decision-making depending on timing? Under

hierarchical structure, it is different which choice opportunities each problem and decision-maker can enter.

Therefore, even if problems or decision-makers intend to move together to another choice opportunity, some

problems or decision-makers cannot move there. That is, the cluster of problems or decision-makers breaks up.

As such a process continues, the combinations of problems and decision-makers in each choice opportunity

change, and decisions are made depending on timing.

Surprisingly, an existing study by an original author implies the garbage can process can be observed

under hierarchical structure. When reviewing March & Romelaer (1976), we realized that the case they

reported was the one under hierarchical structure. Managers devoted their energies to various choice

opportunities on the one hand; on the other hand, teachers engaged in the specific choice opportunity related to

their own interests. In short, accessible choice opportunities differ between the managers and the teachers. This

means access- and decision-structure are hierarchical. As this result, the garbage can process could be observed

in this university organization.

The result that the garbage can process can be observed under hierarchical structure would be appropriate.

Though premature to conclude, this result implies that CMOSM captures something that happens in actual

organizations.

14 This does not depend on load.

Nobuyuki Inamizu

20

Figure 6: The simulation process under hierarchical structure and moderate load


※: Solved problems, ×: Records of decision-makers having been involved in the choice. * Problems and decision-makers cannot attach to the choice opportunity which has not yet entered or has already been decided. Some problems or decision-makers might have no choice opportunities to which they can attach. These problems and decision-makers are not displayed on the computer screen.

* The first and second figures show the appearance at consecutive periods. * Solid arrows: Movements of decision-makers. Dot arrows: Movements of problems. * Decision-makers and problems move here and there, and the combinations of them are changing.

* As 20 periods have passed, decisions have been made by resolution, by oversight, or by flight.


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6. Conclusion 6.1. Contributions to the studies on the garbage can model

In this paper, we rebuilt CMOSM with KK-MAS, making its simulation process visible, and reexamined

the studies on the garbage can model.

The first point of the reexamination was to study the CMOSM process. As Bendor et al. (2001) report, this

paper shows that, under unsegmented structure, decision-makers (and occasionally problems) move together

from choice to choice, and their combinations do not change. In addition, this paper clarifies that

decision-making depending on timing is observed under hierarchical structure. This discovery was not reported

by Bendor et al. (2001).

The second point was whether the results of CMOSM could be observed in actual organizations. This

paper suggests that CMOSM can widely capture how people behave in an organization. The result of CMOSM

under unsegmented structure can explain the office reform of Company X. As the review of March & Romelaer

(1976) suggests, the result under hierarchical structure might actually be seen. When considered inductively,

CMOSM is still effective for analyzing organizations or groups. Therefore, it is premature to assume that

CMOSM has no significance to study.

One of the paths that the studies on the garbage can model should follow is to examine CMOSM again

and to draw implications from this model. In this paper, the office reform of Company X was considered as the

case of unsegmented structure and light load. However, Company X might be faced with a heavy load. The

employees might often let the difficult problems pass. Otherwise, the managers might reduce the load or

temporarily produce the hierarchical or specialized access- and decision-structure. In this way, CMOSM

provides useful suggestions to deepen considerations.

6.2. Suggestions for studies on organizations or groups using computer simulation

A feature of this paper is to focus on the simulation process and to make it visible for examination.

First, making the simulation process visible enables us to examine the working of the model. Therefore,

this is useful to check the results of the simulation. Indeed, a thorough checking the simulation process is not a

novel suggestion. However, this has significance as far as we review March (1991) and relevant studies of the

garbage can model. The visualization should be kept in mind as a way to avoid drawing inaccurate conclusions.

Recently, visualization has become easy, as more accessible multi-agent simulators have been developed15.

Second, visualization is useful in applying the results of simulation to actual organizations. In this paper, 15 Although the simulation process can be reconstructed from its numerical results, this is demanding if the model is complex. In this regard, when using KK-MAS, we have only to set “two-dimension map” and add some codes.

Nobuyuki Inamizu

22

demonstration of the simulation process promoted conversations with the executive, and reminded us of a

phenomena corresponding to the simulation. Although Yamakage & Hattori (2002) have included articles using

KK-MAS, the studies being similar to this paper cannot be seen. The demonstration might be a new method

related to the photo elicitation method, which was introduced by Harper (2000). There should be more studies

like this paper, because a typical feature of multi-agent simulators is to visualize a simulation process16.

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