Subject: REPLY Human Behavior in System Dynamics Models (SD3337)
Date: Tue, 24 Jul 2001 15:01:40 -0600
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Jack Ring asks about two very different situations and the applicability of
SD in addressing them. The first is a straightforward problem of dealing
with firings based on "personality conflicts." The second is a more
problematical exploration of worker enthusiasm. Before I head into this, I
feel the irrepressible need to introduced it with (another) background
harangue on the SD approach.
SD is a problem-oriented approach. Its goal is not to model the system but
to only include those aspects of the system important to understanding and
resolving the problem. This relates directly to the insight of another
recent listserve thread concerning the order of systems. The equations are
not there to describe assumed results but rather to explain the causes of
the results - and thereby provide the opportunity to intervene and produce
more desirable results.
While the understanding of the physical and financial aspects of a system is
much more uncertain and limited than we would often like to acknowledge, the
understanding of human behavior is even more ambiguous. If the human
behavior either does not produce dynamics relevant to the problem solution
or can be treated as a relatively fixed, uncontrollable entity, then it can
be subsumed in the mathematical structure of the model. Many problems
actually conform to this situation and human elements (the humans having the
problem) over-rate their importance to the underlying dynamics. If the
human behavior is surmised as the cause of the problem or an integral part
of it, then those relevant aspects of human behavior do need to be
simulated.
All science is inexact. Each day, new more refined experiments discover new
unexpected features (mechanisms) in cosmology, quantum mechanics, genetics,
etc. Occasionally, the new findings lead to an overthrow of our
understanding of some aspect of reality (See Stermans human-behavioral
modeling of Thomas Kuhns Structure of Scientific Revolutions.)
Nonetheless, the old more aggregate understanding was often useful to
address pressing problems (for example, Newtonian physics versus Relativity
and classical physics versus quantum mechanics). The issue is not fully a
question of right or wrong but one of rationality. Is there a causal
(hypothesized) explanation of responses (cause and effect) consistent with
measured conditions? If the result cannot be quantified and measured - even
qualitatively, then it is irrelevant (useless) for our purposes and
irrational from our paradigms perspective.
Physical and financial dynamics have a limited number of viable hypotheses.
Often all of any conflicting explanations can be "included" in the analysis
process. The need for self-consistency (uniform rationality) and the
comparison of simulated output to simulated output often falsifies (see Karl
Poppers The Logic of Scientific Discovery) a large number of the competing
explanations. The remaining explanations can be represented as alternative
scenarios that cannot be discarded. We find that multiple self-consistent
presentation often lead to the same policy conclusions. Econometricians
call this encompassing. By this they mean there is a more generalized
data-generating process (DGP) that could be recognized/discovered or that
the various "perspectives are basically describing the same process but
from a different perspective. When the alternative perspectives do not lead
to the same solution, the perspective most consistent with the stakeholders
paradigm seems to usually wins out. Some things in life must remain
uncertain.
Human behavior adds further dimensions to the complexity of modeling. There
are too many degrees of freedom given the data and much remains to be
understood about even generic aspects of human behavior. We are still in
the Dark Age of cognitive/emotive science. We are left to assume that
either what we do not fully understand is irrelevant or we can do the best
we can to rationally include it. This "best" provides the best chance of
successfully solving the problem. The "best" may be far removed from the
ever-elusive "truth." As was the case in early scientific discovery,
aggregate perspectives (thermodynamics, for example) provide a means to deal
with the aggregate phenomena of concern. We do not need to know the
dynamics of individual atoms. We only need to have a relevant understanding
of the aggregate behavior relevant to our problem. Fortunately, in social
science, if our knowledge is limited to aggregate problems (those that are
big enough to impact us) so too is our cognition of underlying detailed
dynamics. We have an excuse to only deal at the aggregate level because
that is all we can perceive. I often use the concept of coping-skills to
contain these aggregate mechanistic behaviors. Coping skills are explicit
in the SD work of Ulrich Goluke on alcoholism and implicit in the expansive
SD work of Jack Homer and others on worker burnout. This aggregation
produces results consistent with experienced human behavior (therefore
useful) and provides a tractable means to test policy (therefore efficient).
With this background, I can then address the points Jack brings up. The
first issue is the corporate and economic loss (waste) associated with
"firings" based on personality conflicts. (I have to believe that some
graduate student or consulting firm has simulated this HR issue already but
need some other SD practitioner needs to help me out on that point.) The
first value of SD is to recognize the system context of the "problem." From
a system perspective, either there is a management personnel problem or
there is a position problem. If I am in charge of a suicide-prevention
hot-line or an air-traffic controller shift, the personality traits are
important. If the problem of under-staffing becomes critical, I need to
either change the definition of the position to allow more flexibility in
staff psyches (as in the tiered help-line services for many software
companies) or train (enhance the coping skills of) the employees before they
are placed on the front line. Properly designed field-tests or surveys can
determine the (relative) validity of the dynamics assumptions related to the
problem and proposed intervention.
If the problem is with a manager or management practice, then one or both of
those two issues are readily addressed. Management practice is readily
surmised using panel data from other firms. Many consulting firms have
"best practice" divisions designed solely to recognize the organizational
structures that work for given conditions. It still, however, remains the
domain of the system dynamists (my biases are showing) to give a casual
explanation of why these practices produce the results. If the problem is
with the managers who do the firing, the "coping skill limitation" is on the
managerial end. The manager may feel threatened, overwhelmed, victimized,
or simply powerless. Interviews with both thriving and ineffective managers
can help develop causal hypotheses to test via simulation. Additional
training, re-specification of the position, separation of the position into
additional hierarchies, aggregation/elevation of departments to a more
viable entity, etc. all come to mind. Each of these can be tested using the
best understanding of cause and effect against the evidence. (SD quackery
can misrepresent this approach when "practitioners" include correlation
equations producing dynamics rather than restrict themselves to the use of
only causal structures.) I would argue that the causal use of SD is
equivalent to respected historical-study approaches where value is derived
in producing a consistent and useful story. SD, in practice, is a language.
Any causal explanation (story) can be readily represented in an SD model.
There is no limitation for modeling human behavior.
The second issue Jack brings up is workforce motivation. The problem here
is that "work force motivation" is not a problem statement. It may or may
not be a contributor of a problem (recent studies indicate dissatisfied
workers are more productive: Lavis, C.A. & Sinclair, R.C. Are happy workers
better workers? Examining the effects of mood on productivity. Proceedings
& Abstracts of the Annual Meeting of the Midwestern Psychological
Association, 71, 11, 1999). If we are in the realm of SD, the issue is, for
example, productivity.
SD does not find mechanisms but tests hypothesized mechanisms. Humans
cannot (yet) relinquish their responsibility for their ignorance. (Many)
humans tend to be creative and can readily posit alternative "causal
explanations." The SDer can then map and reconcile these into a
self-consistent framework as noted above. The resulting self-consistent
causal representation (model) is then the best hope for rational policy
development. To be concrete, the "mechanism" Jack presents would appear to
fall into the conceptual domain of coping skills. When coping skills are
not nurtured or challenged, they atrophy and coping skills decline. When
overpowering forces prevent compensating actions, coping skills decline.
Appropriate challenges and discretionary flexibility reinforce coping
behaviors. Personnel in repetitive processes often have no discretionary
capabilities -- they can only follow the inflexible rules. In many of our
projects, we find these "line" workers to be much more stressed than senior
management who are supposedly dealing with the crises that made them so
desperate that they reluctantly resorted to calling us. Individuals tend to
operate near the left-side of the peak of their coping capability. The
approach is then to increase the coping skill level. This may include
reward systems that make failure less powerful than a success (as in the
case of R&D efforts) or it may require changes to the workplace that
reinforces self-actualizing behaviors. I am sure there are many other
causal possibilities, but the key is that SD is fully capable of modeling
the problem and the relevant aspects of the system containing it.
Jacks concluding remarks indicate that the focus of his interest is in group
dynamics, project work climate, organizational cultures, and policy setting
as a function of team selection. Organizational learning is another growing
aspect of SD efforts and possibly other listserve participants may directly
contact Jack on their efforts. Similarly, work-climate is a significant
focus for many consultants on the listserve. Basic group dynamics and human
behavior are often put in the domain of action science and action research
(I am just newly aware of this myself). See, for example,
http://www.actiondesign.com
esources/ and
http://www.actionscience.com/actinq.htm This area is related to Senges work
on the Fifth Discipline.
Lastly, I just want to comment on the "team selection" process. I think it
needs again to be in the context of a problem and that a generic
representation of "team selection" will remain elusive. Selecting team
members based on the problem being addressed has relevance and a definable
scope. The denotation of simply "team selection" is a general "SYSTEM"
issue and the SD paradigm argues that neither it nor any other paradigm can
give meaning to the concept of "modeling a system."
Another unfortunate soapbox episode thus ends.. I need a life
George
George Backus
Policy Assessment Corporation
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Arvada, CO 80004-3621
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Email: George_Backus@ENERGY2020.com