Table of Contents
Dynamics of Managerial Intervention
in Complex Systems
Kambiz Maani, Anson Li
School of Business
The University of Auckland
Private Bag 92019 Auckland
New Zealand
Phone: (64-9) 373-7599, voice 88813
Fax: (64-9) 373-7566
Email: k.maani@ auckland.ac.nz
ABSTRACT
Research, as well as decades of working with managers from diverse cultures, nationalities,
and industries, has exposed consistent counter productive patterns of behaviour in relation to
decision making in complex systems. In this regard, managers appear to exhibit an
unmistakeable tendency to “over intervene” in the systems (companies, organizations,
communities, etc) for which they are responsible hence generating unnecessary fluctuations
and instability in their organizations. Maani, et al (2004), and Sterman, et al (1989; 2000)
have studied these phenomena in experimental and simulated environments respectively.
Anecdotal evidence, as well as research results, highlights a number of mental models and
assumptions commonly held by managers. These are outlined below:
1. Dramatic change should lead to dramatic (positive) results. Our research shows that
often the opposite happens.
2. The more change initiatives (interventions), the better the results. A gain our research
shows that “over-intervention” is counter-productive.
3. Managers often ignore “soft” variables (eg, morale, stress, burnout, loyalty, etc) to the
detriment of their organizations. Y et, “soft” variables are powerful predictors of long-
term performance.
4. Managers are often oblivious to “systems delays”. Lack of awareness/attention to
delay undermines performance and inhibits system stability.
5. Organizations and managers often judge performance by short-term results.
Experience shows that expectation of short-term results is unrealistic and misleading
and can lead to counteracting outcomes as performance often declines before it
improves.
6. Organizations and managers tend to use too many performance measures (ie, KPIs).
As what gets measured impacts performance, excessive and misguided measures can
lead to poor results and unexpected consequences.
7. Managers generally focus on “what to dos”. It is not enough to know what needs to
be done. Order and timing of actions are as important as the actions themselves.
The propositions and observations outlined above collectively form the research questions
posed in this paper: “How do the style (extent) and frequency of change and the
interpretation of feedback affect the outcomes of interventions in organizations?” In this
research, realistic simulation models of organizations (Microworlds) are employed as proxy
for complex systems. Research subjects comprise MBA and graduate business students and
practicing managers. The paper deals with systems thinking theory and practice in complex
decision-making and their implications for transforming managers and organizations to
achieve sustainable success.
Key words: Systems Thinking, Complex Decision-Making, Dynamic Behaviour, Change
Management
INTRODUCTION
In the past three decades, much research has explored the complexity of decision-
making under the ‘bounded rationality’ of human mind. This includes studies by
Simon (1957, 1979, 1987), Morecroft (1983, 1985), Senge (1990), and Sterman
(1989, 2000). The latter three have related this dilemma to systems thinking theories.
According to Richmond (1994), systems thinking is “the art and science of making
reliable inferences about behavior by developing an increasingly deep understanding
of underlying structure”. By understanding problem situations with a systems
perspective, a more holistic understanding can be achieved in terms of the causal
relationship between decisions, interventions, and their expected results. Under
bounded rationality, it is not realistic to expect that interventions will yield the
expected (and only the expected) results. Further, decisions made with good
intentions do not always result in the favourable outcomes anticipated by the decision
maker.
Likewise, a substantial amount of research has been carried out in relation to the
dynamics of decision making with a systems thinking perspective. This includes
“Limits to Growth” (Meadows, 1972), “System Dynamics: Portraying Bounded
Rationality” (Morecroft, 1983), “Beyond the Limits” (Meadows, 1992), and the
“Improvement Paradox” (Keating et al., 1999). While these studies provide
significant insight into the formulation and outcomes of decisions, the empirical work
in the area of decision dynamics and interventions in complex systems remains
elusive.
RESEARCH OBJECTIVES
The objective of this study is to explore the dynamics of “interventions” in complex
systems. To our knowledge there are no serious research, e.g., in System Dynamics,
which investigates the causes and consequences of over-intervention. This paper, as
part of a broader research, aims to address the apparent gap in this filed.
In the context of this research, intervention is broadly defined as any action that
changes the state of a system, and is further quantified by the number (Frequency) and
the magnitude (Style) of change (intervention). The research will involve empirical
testing with informed participants using simulation microworlds. Research subjects
comprise graduate business students and practicing managers.
Through a deeper understanding of the dynamics of interventions the research seeks
to identify and derive “pattems of interventions” which would assist in decision-
making and effective formulation and implementation of interventions in complex
systems.
BOUNDED RATIONALITY
According to Simon (1957), “bounded rationality is a property of decision making
that reflects people’s cognitive limitations. Individuals faced with complex choices
are unable to make objectively rational decisions”. The reasons for this are as follows:
1. They cannot generate all the feasible alternative courses of action;
2. They cannot collect and process all the information that would permit them to
predict the consequences of choosing a given alternative; and
3. They cannot evaluate anticipated consequences accurately and select among
them.
Morecroft (1983) carried out a study on the philosophy of human decision making
expounded by the Carnegie School.’ “Underlying the work of the School is the
powerful notion that there are severe limitations on the information processing and
computing abilities of human decision makers. As a result, decision making can never
achieve the ideal of perfect (objective) rationality, but is destined to a lower level of
intended rationality.” (Morecroft, 1983)
Along with the above arguments related to bounded rationality, Morecroft (1982,
1985) identifies six common practices that underlie the shortcomings of the human
decision making process. They are:
1. Factored (fragmented) decision making
Complex issues are divided up into pieces (eg, disciplines, sections, departments,
etc) to facilitate decision-making, as “they cannot be handled by an individual”.
2. Partial and certain information
Decision makers tend to use “only a small proportion of the information that
might be relevant to full consideration of a given situation”. They would also
“avoid the use of information that is high in uncertainty”. This tends to focus the
decisions on problem symptoms and locally optimum solutions.
3. Rules of thumb / Routine
This refers to situations where decision makers, under time pressure, resort to
“quick fixes” in order to rectify a situation as quickly as possible. Quick fixes
often result in “backfire” or unintended outcomes.
4. Goals and incentives
Focus on certain goals and incentives could compromise other areas and
undermine the performance of the larger system.
5. Authority and culture
Culture and tradition provide powerful predetermined frameworks for decision
makers (i.e. mindset, mental model). Through customary routines and commands,
prevailing values and traditions are transmitted to all and hence get reinforced and
further ingrained.
6. Basic cognitive processes
“People take time to collect and transmit information. They take still more time to
absorb information, process it, and arrive at a judgment. There are limits to the
amount of information they can manipulate and retain. These cognitive processes
can introduce delay, distortion, and bias into information channels.”
To deal with the above shortcomings, many authors have suggested ways to improve
the effectiveness of human decision-making. These include, among other tools,
management and computer frameworks (Gilberto, 1995, Cayer, 2001), computer
simulation models (Simon, 1987, Sterman, 1988), and the use of systems thinking in
decision-making (Senge, 1990, Maani, et al 2004).
STUDIES OF INTERVENTIONS
MISPERCEPTION OF FEEDBACK
A classic work in this area is Sterman’s research (1989) in relation to the
“misperception of feedback”. A simulation model, known as the “Beer Game”, was
used with groups of participants to investigate their interpretation of information
feedback and the effects on the interventions derived.
“The decision making task is straightforward: subjects seek to minimize total costs
by managing their inventories appropriately in the face of uncertain demand.”
(Sterman, 1989) In sucha “simple” environment, however, things did not always go
as planned for most participants, due to the rich simulated environment, which
contains “multiple actors, feedbacks, non-linearities, and time delays.” (Sterman,
1989) Similar to Morecroft’s and Simon's idea about factored decision making, “the
interaction of individual decisions with the structure of the simulated firm produces
aggregate dynamics which diverge significantly and systematically from optimal
behavior.” (Sterman, 1989).
The findings of the study are summarised into the following points (Sterman, 1989):
- Subjects failed to account for control actions, which had been initiated but not
yet had their effect.
Subjects were insensitive to feedbacks from their decisions to the
environment.
- The majority attributed the dynamics they experienced to external events,
when in fact these dynamics were intemally generated by their own actions.
The subjects’ open-loop mental model, in which dynamics arise from
exogenous events, is hypothesized to hinder leaming and retard evolution
towards greater efficiency.
THE IMPROVEMENT PARADOX
Keating et al. (1999) carried out a study of the effectiveness of improvement
programs. The motivation for the study arose from the fact that “most attempts by
companies to use them [improvement programs] have ended in failure” (Easton and
Jarrell, 1998 in Keating et al., 1999), and that even “successful improvement
programs have sometimes led to declining business performance, causing layoffs, low
morale, and the collapse of commitment to continuous improvement.” This dilemma
was termed the “Improvement Paradox”.
The study was carried out on major companies to understand why improvement
programs often fail. The findings suggest that “the inability to manage an
improvement program as a dynamic process - one tightly coupled to other processes
in the firm and to the firm’s customers, suppliers, competitors and capital markets - is
the main determinant of program failure. Failure to account for feedback from these
tightly coupled activities leads to unanticipated, and often harmful, side effects that
can cause the premature collapse and abandonment of otherwise successful
improvement programs.” The study, however, does not suggest that improvement
programs are ineffective in terms of improving organizations. In fact, the authors
point out that “firms with developed quality programs significantly outperform their
counterparts in profitability, share price and retum on assets.” The problem lies in the
suitability of these programs and the style with which they are implemented.
LEADERSHIP AND INTERVENTION
Ina recent HBR article, Kanter (2003) reports on several companies which have
experienced major declines in their fortunes, declines which have been successfully
reversed by the interventions of their new CEOs.
These companies, although from different industries and differing in size, experienced
similar patterns of decline in their business. Often decisions were made by various
functions or divisions (as in “factored decision making” ") to employ quick fixes (as
in “rules of thumb”) to various problems in order to achieve short-term goals within
tight time limits (as in “goals and incentives”). For example, a common practice at
Gillette was to offer “discounts to retail customers at the end of a quarter in order to
move products and achieve sales targets, thus sacrificing margins and jeopardizing the
next quarter’s sales”.
The author has suggested that the use of common practices as rules of thumb (as in
the Gillette case) is very common in troubled companies. These short-run solutions
usually make the situation worse in the longer term. For instance price cuts from
discounts, although they would be effective in increasing sales, would also reduce the
funds available for marketing, which increases the organization's reliance on the
promotional deals. Customers will also know that they can wait until quarter’s end to
get even better deals.
The resulting deterioration in morale and work culture can be termed “learned
helplessness”. People in the organization feel that there is little they can do to make a
difference in the company and therefore become passive. This in tum reinforces the
decline of the organization - a vicious cycle that could lead to ultimate downfall.
RESEARCH METHODOLOGY
In order to explore the complex dynamics in managerial interventions, an
experimental research approach in conjunction with a computer simulation model has
been used in this study.
The use of simulation models in experimental research as an alternative to laboratory
and field experimentation has become common. In these simulations, participants are
exposed to real-world experiences where “manipulation [of independent variables]
and control are possible ... [and] the course of activities is at least partly governed by
the participants’ reactions to the various stimuli as they interact among themselves.”
(Sekaran, 2000)
EXPERIMENTATION TOOL
The experimentation tool used in this research is a computer simulation model known
as the Service Quality Microworld (SQM), developed by MIT System Dynamics
Group and used by the authors for several years in executive courses. SQM simulates
the operations of a generic service company. The simulation starts at a “steady state”
where incoming orders, orders completed, work backlog, rework, hiring, personnel
tumover, time pressure (employee), monthly profit, and monthly expenses are held at
a constant rate. Appendix A shows the partial Causal Loop Diagram involving the
input variables.
The research subjects are graduate business students who were invited to take part in
the experiments. At the time of this writing 15 participates had completed the
experiments - they comprise the sample group for this report.
Each experiment session lasted 2-3 hours during which the participants could
manipulate the values of three “input” variables: net hiring (monthly), quality goal,
and production goal, thus causing changes in the ‘output’ variables, listed above,
through the complex and dynamic relationships amongst them.
The simulation is advanced on a monthly basis for up to 60 months. Players can
manipulate any/all of the three input variables over time in an attempt to achieve
defined goals, such as maximizing cumulative profits, minimizing rework, or
improving productivity. Numerous results are generated instantaneously through
SQM reports and graphs.
At the outset of the experiments the participants were encouraged to use the learning
cycle comprising: “Conceptualize - Experiment - Reflect” in developing their
decisions and interventions (Maani, et al, 2000).
DATA COLLECTION
In the experiment sessions, the participants were required to perform certain tasks to
achieve the stated goals using the simulation microworld. The subjects worked
individually during the experiments with no breaks so no information exchange and
“interaction effects” were expected to occur. Data were collected on:
- Demographical information about the participants;
- Strategies devised by participants for carrying out the task(s) and/or achieving
the goal(s) in the simulation model;
Actual interventions carried out in the experiment on the simulation model;
Outcomes and results on the simulation model; and
Participants’ interpretation and comments relating to the interventions and
outcomes/results.
The above include both quantitative and qualitative data, which will facilitate a
triangulated perspective of the research questions.
RESEARCH EXPERIMENTS
For the experiments the participants were required to achieve the stated goal of
maximizing cumulative profits over the period of 5 years by implementing various
interventions with respect to the three input variables, namely, Net Hiring, Production
Goal and Quality Goal.
There were two separate exercises involved in each experiment session:
Exercise One: Participants were asked to achieve the goal by intervening with only
one of the three variables (Net Hiring, Production Goal, and Quality Goal) over the
course of the 5 years in the simulation. They were free to choose any of the above
three.
Exercise Two: Participants were asked to achieve the goal by intervening with any
combination of the three variables over the course of the 5 years in the simulation.
In both exercises, participants were asked to develop a strategy before starting the
simulation. The subjects were asked to record their strategies on the worksheets
provided, which shows a detailed log of their decisions, actions and results. Also, they
were asked to predict the likely behaviour pattern of their chosen KPIs over the
course of the simulation. Subjects were monitored inconspicuously during the session.
Once the planning step was completed, the participants were asked to record a
schedule of their interventions on a time line. That is, when and how much change in
the chosen input variables they were planning to implement.
At the end of the simulation run, they were required to record the stated outcome of
the experiment (i.e., the cumulative profits at the end of year 5), and comment on the
result as well as the process. This information was also recorded on the appropriate
worksheet.
Of particular interest to the research question were two key measures by which
“intervention” was quantified. First, the Frequency Variable measured the number of
changes made during the course of the experiment. Second, the Style Variable, a
measure of average percentage change, representing the extent and magnitude of
changes made. The value of each variable, recorded by the subjects themselves, was
converted into an index (with a base value of 1 for the initial default value). The style
variable is then calculated as the average change in the index during the five years of
the simulation. Hence, the larger the index, the larger the degree of intervention made
by the subject in the system.
Following the experiments, the strategies of the participants were examined by
scrutinizing the graphic outputs of their KPls, the three input variables (Net Hiring,
Production Goal and Quality Goal), and other measures computed in the simulation,
such as Total Personnel, Orders Completed, Actual Quality, Time Pressure and
Rework. As SQM isa systemic model, all these factors are dynamically interrelated.
These outcome patterns were then studied against the original strategy developed by
the participants to examine whether:
1. They had adhered to their original strategy throughout the experiment; and
2. To what extend the simulation results were consistent with their anticipated
outcomes.
Any discrepancies from the original strategy and their expectation were noted and
studied closely to find out more about the mental model of the participant, and how
the information ‘feedback’ influenced the participant’ s decisions during the course of
the simulation.
PRELIMINARY RESULTS
The full results of the experiments for Exercise One are summarized in A ppendix B.
From this table, the best and worst results (cumulative profit over 5 years) are shown
below (Table 1) and contrasted with the base-line performance (steady state - no
change).
Table 1 - Best and worst sample results compared to base-line performance
Sample results Style Frequency Cumulative Profits
Worst performer 0.718 17 - $20,294,781
Best performer 0.077 3 $13,129,626
Baseline 0 0 $13,125,000
performance
A cursory examination of the results shown in Appendix B reveals some consistent
and powerful patterns of causal relationships. For example, the notions that the larger
the number of interventions (the “Frequency” variable), and the larger the extent of
change (the “Style” variable), the poorer the outcomes (“cumulative profit”) are
strongly evident.
To test this statistically, a multiple regression model of Frequency and Style as
independent variables against Cumulative Profit as dependent variable was run which
confirmed the above observations. The multiple regression results are summarized in
Tables 2 and 3 below:
Table 2 - Output from regression analysis on Style, Frequency, and Cumulative Profits
(Exercise One)
Experiment | n R? F Sig. F | Variable | Coefficient | t-stat p-
value
Exercise One | 15 | 0.5646 | 7.7791 | 0.0068 | Style -21196968.67 | -3.0365 | 0.0103
Frequency | -418431.7985 | -3.5647 | 0.0039
Table 3 - Output from regression analysis on Style, Frequency, and Cumulative Profits
(Exercise Two)
Experiment | n R? F Sig. F | Variable | Coefficient | t-stat p-
value
Exercise Two | 15 | 0.4701 | 5.3238 | 0.0221 | Style -42269496.7 | -2.8175 | 0.0155
Frequency | -260310.051 | _-1.9804 | 0.0711
As the table strongly indicates, the model as well as the two independent variables is
highly statistically significant even at the lower significance level of 0.0711 (to higher
significance level of 0.0039). What is remarkable and did surpass the author’s
expectation is the high level of R’ achieved with only two explanatory variables
within a sample size of 15 participants! That is, in the simulation experiments, 47% to
57% of variations in cumulative profits, over a 5-year period, could be reliably
explained by the style (i.e., extent) and frequency of managerial interventions. A
closer inspection of the participants’ monthly profit patterns (not shown here), adds
considerable explanation power to the regression models. Further, taking into account
other qualitative variables (see summary table for Exercise One in Appendix A) adds
further prediction capability for sustained profit performance in the long term. This
aspect will be explored in future papers. It must be noted that, in the above
experiments, variations in performance are primarily due to the interaction dynamics
between the input variables and internal system variables and do not arise from
stochastic elements in the model.
These results, although at this stage still preliminary, go a long way towards refuting
the first two and the most fundamental research propositions of this study, namely:
1) Extent of change (Style): Dramatic change should lead to dramatic (positive)
results.
2) Frequency of change (Frequency): The more change initiatives (i.e., more
interventions), the better the results.
In other words “over-intervention” is counter-productive.
Currently, further research is in progress to test this theory using other microworlds as
well as investigating real life case studies to validate the robustness of the preliminary
results reported above. Should the results hold true under different microworlds and
teal cases then they would have far-reaching managerial and organizational
implications and could shed new light on what would constitute a theory of
intervention in social systems.
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11
APPENDIX A
Partial Causal Loop Diagram involving the input variables for SQM
Revenue
. NL Work
Monthly Compete
Profits
)-
0
Work
Actual
Production_Goal ia si 27 ms Quality
Customer aol Goal
Orders
ye
Lo Pressure o
s
Turnover
Q
Total
Personnel
s
ee Employees Hired
Monthly so (het Hiring
Expenses
APPENDIX B
Experiments Summary: Exercise One
ID | Style Frequ- Soft Appreciation of Delays Short Term Results Sequence & Timing | KPIs Cumulative
ency Variables Profits
Noted
01 | 0.077 3 bb ig Not mentioned Reacted to the reduction in Profits by | Good (increase capacity | Profits, $13,129,626
Major lowering HR early). TP, RW
changes
02 | 0.507 43 AQ ‘Not mentioned (the adverse Participant did not react to the fall in | N/A Profits, $-12,617,236
Big effects of the increase in QG came | Profits immediately. (Should have RW
increments, after a short delay) ‘stopped increasing QG).
for
prolonged
periods
03 | 0.028 43 AQ Not mentioned (delayed effect of Did not react to the drop in profits: NA Profits, $2,566,367
Major increased PG) immediately. oc,
changes RW,
with small AQ.
adjust-
ments
04 | 0.718 a7. AQ, TP, Not mentioned (build up of TP Responded to drop in profits by NA Profits, $-20,294,781
Big “E due to increased QG came ata increasing QG PT,
changes delay) RW,
WB,
OC, AQ
05 | 0.031 52 TP, AQ Not mentioned (Adverse effects of | N/A Prolonged increase in. Profits, $-16,581,752
Small ‘TP came at a delay) PG, resulting in WB,
changes negative effects. RW,
TP, PT,
AQ
06 | 0.090 37 %E, TP Not mentioned (delayed effects ‘Tried to lower PG to gain short term | Lowering of PG at Profits, $-7,751,501
Big & from build up of TP) results in lowering TP. Resultedina | beginningwas unwise | OC,
small failure RW,
changes WB
13
07 | 0.01 59 AQ, TP Not mentioned (delayed effects of | Treated the initial short term increase | N/A. The feedback from | Profits, | $3,970,629
Incrementa increase in TP) in profits as sustainable. Assoonas__| the system wasignored. | PT, TP
1 profits fall, QG was reduced.
(continuou
s) without
much
reviewing
08 | 0.073Small | 17 TP Not mentioned (build upof TP) —_| Responded to the lack of short term | N/A Profits, | $7,978,145
changes increase in profits by reducing QG. TP, PT
with one Unwise.
major
change
09 fa 5 Not Not mentioned (delays in Did not react according to short term | Increase in QG is too Profits, | $10,831,849
Major mentioned | improvements and TP) results. Strategy implemented big & frequent, since | AQ
changes regardless of the outcome employees are not
experienced
10 | 0.458 4 Not NA ‘Worsened situation was not noted NA Profits, | $-5,071,931
mentioned until the 5” year. Radical AQ
interventions were deployed then.
11 | 0.028 5 Not Not mentioned Reaction to slight drop in profits NA Profits | $11,876,724
Small mentioned during months 51-53 by major drop
changes in QG. Too excessive
12 | 0.077 6 TP Not mentioned (increase in HR **The fall in profits at the beginning | N/A Profits, | $11,688,783
Small has led to an increase in OC which | has led to an increase in HR, when in oc, TP
changes reduces WB, contributing to the —_| fact, it is the extra hiring that has
reduction in profits at end) caused the fall in profits.
13 | 08Big 5 TP Not mentioned (the delayed ‘The initial success in profits hasled | N/A Profits, | $5,303,774
changes effects of the increased initial QG) | to more increase in QG AQ
14 | 0.246 5 %E Noted the delay in training of Did not act according to short team | Good. Waited for Profits, | $11,294,976
rookie employees results effects to happen (%E) | OC, AQ
15 0.718
Majo
14
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