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Why has it NOT to do with Information Science?
The understanding of Information Science rests on the understanding and definition of “information”
– Shannon’s engineering perspective (physical transmission)
– Context / meaning of “information”
– Buckland (“process” - “thing” - “knowledge”)
– Quigley and Debons (just text answering to “NEOT” versus “WY”)
– Buckland (“observation of phenomena that have the capacity to be informative”)
– Norton (“fundamental link among all what we are, know, and do not know”)
MIT Field Trip, 4/20//2001 Hans J. (Jochen) Scholl Slide 2

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Lipetz ( “facilitation of utilization of records”)
Georgia Institute of Technology (1961)
– “The science that investigates the properties and behaviors of information, the forces governing the flow of information, and the means of processing information for optimum accessibility and usability. The processes include the origination, interpretation, and use of information. The field is derive from or related to mathematics, logic, linguistics, psychology, computer technology, operations research, the graphic arts, communications, library science, and some other fields” (Shera &
Cleveland)
Paisley (“IS is not retrievology”)
Borko’s list of nine application fields (…(6) system design, (7) analysis and evaluation, (8) pattern recognition, (9) adaptive (and self-organizing) systems)
Otten and Debons (“Metascience - framework for all information-oriented sciences”)
Skovira (“Pluralism in understanding of IS ”)
MIT Field Trip, 4/20//2001 Hans J. (Jochen) Scholl Slide 3

Parent Disciplines
Mathematics
Computer Science
Linguistics
OR
Public Administration
Psychology
Business Administration
….
Library Science
MIT Field Trip, 4/20//2001
Metascience
An Information Scientist is trained in and has a natural interest in cross studies and multi-method approaches
Hans J. (Jochen) Scholl Slide 4

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– Mathematical or linguistic models : rigorous versus flexible
– Quantitative / qualitative
– Underlying principle:
"(1) the cause precedes the effect in time, (2) there is an empirical correlation between them, and (3) the relationship is not found to be the result of some third variable” (Babbie,
1999)
– Best applied to relatively simple, linear systems and when coping with relatively narrow / limited data spaces
– Vast data spaces
– Flexible AND
– Rigorous
MIT Field Trip, 4/20//2001 Hans J. (Jochen) Scholl Slide 5

p i with i=1,…n and p i
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P
m j
r k with j=1,…m and m j with k=1,…r and r k
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R
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M
What r
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R found through m
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M when applied to explain a certain p
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P
• correspond / are similar / are equal?
• differ / contradict?
• neither correspond nor contradict / complement ?
What r
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R can be expected when combining certain m
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M to explain / triangulate a certain p
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P?
MIT Field Trip, 4/20//2001 Hans J. (Jochen) Scholl Slide 6

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What dynamic problems have been explained by means of which methodology?
What where the findings in cases when more than one methodology was applied to a dynamic problem?
What are the insights from comparing the findings?
What are strengths and limitations of the methodologies used when applied to explain the dynamic problem at hand?
What are potential benefits of multi-method research designs?
Case in point: The Beer Distribution Game
MIT Field Trip, 4/20//2001 Hans J. (Jochen) Scholl Slide 7

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Linearity
– A relationship is linear if the effect on a dependent variable of a change of one unit in an independent variable is the same for all possible such changes
Nonlinearity (ergo)
– A relationship is nonlinear if the effect on a dependent variable of a change of one unit in an independent variable is NOT the same for all possible such changes
Nonlinearity
– If f is a nonlinear function or an operator, and x is a system input (either a function or variable), then the effect of adding two inputs, x1 and x2, first and then operating on their sum is, in general, not equivalent to operating on two inputs separately and then adding the outputs together; i.e. . Popular form: the whole is not necessarily equal to the sum of its parts. Dissipative nonlinear dynamic systems are capable of exhibiting self-organization and chaos. (Nonlinear Dynamics and Complex Systems Theory,
Glossary of Terms - http://www.cna.org/isaac/Glossb.htm#Nonlinearity - access date
04/09/2001)
MIT Field Trip, 4/20//2001 Hans J. (Jochen) Scholl Slide 8

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Nonlinearity
– “a system is nonlinear if it contains a multiplication or division of variables or if it has a coefficient which is a function of a variable (Forrester, 1968)
“Degree of (a system’s) nonlinearity
– implies the number of policies in the system that are nonlinear (ibid.)
Complexity in systems
– multiple interconnected positive and negative feedback loops containing nonlinearity (ibid.)
Complex systems
– have seven characteristics: (1) counterintuitive, (2) insensitive to many parameter changes, (3) resistant to policy changes, (4) pressure or leverage points, (5) compensate for externally applied pressure, (6) short-term behavior may differ from long-term behavior (7) tendency to low performance (social systems)
(Forrester, Urban Dynamics, 1969)
MIT Field Trip, 4/20//2001 Hans J. (Jochen) Scholl Slide 9

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Emergence
– “… a product of coupled, context-dependent interactions (which) , and the resulting system, are nonlinear “ (Holland, Emergence, 1999)
Nonlinearity
– “The behavior of the overall system cannot be obtained by summing the behaviors of its constituent parts” (ibid.)
Complexity
– (generated) emergent system behavior based on simple laws
MIT Field Trip, 4/20//2001 Hans J. (Jochen) Scholl Slide 10

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Stanislaw Ulam reportedly said (something like) "Calling a science 'nonlinear' is like calling zoology 'the study of non-human animals’ (J.D. Meiss, sci.nonlinear FAQ )
Nonlinearity
– In geometry, linearity refers to Euclidean objects: lines, planes, (flat) threedimensional space, etc.--these objects appear the same no matter how we examine them. A nonlinear object, a sphere for example, looks different on different scales-when looked at closely enough it looks like a plane, and from a far enough distance it looks like a point.
– In algebra, we define linearity in terms of functions that have the property f(x+y) = f(x)+f(y) and f(ax) = af(x). Nonlinear is defined as the negation of linear. This means that the result f may be out of proportion to the input x or y. The result may be more than linear, as when a diode begins to pass current; or less than linear, as when finite resources limit Malthusian population growth. Thus the fundamental simplifying tools of linear analysis are no longer available (ibid)
MIT Field Trip, 4/20//2001 Hans J. (Jochen) Scholl Slide 11

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Complex Systems
– are spatially and/or temporally extended nonlinear systems characterized by collective properties associated with the system as a whole--and that are different from the characteristic behaviors of the constituent parts.
– While, chaos is the study of how simple systems can generate complicated behavior, complexity is the study of how complicated systems can generate simple behavior. An example of complexity is the synchronization of biological systems ranging from fireflies to neurons.
– In these problems, many individual systems conspire to produce a single collective rhythm. (ibid.)
MIT Field Trip, 4/20//2001 Hans J. (Jochen) Scholl Slide 12

Boids’ Three Rules (Craig Reynolds)
1.
2.
3.
Maintain a minimum distance from other objects in the environment, including other boids.
Match velocities with boids in its neighborhood.
Move toward the perceived center of mass of boids in its neighborhood.
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MIT Field Trip, 4/20//2001 Hans J. (Jochen) Scholl Slide 13

Deterministic chaos: Lorenz’s weather equations
40
0
X dX dt initial x
S
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B
Y dZ dt
R dY dt
Graph for Y
-40
0 10 20 30
Time (time)
Y : base28001
Y : base27999
Y : base28
MIT Field Trip, 4/20//2001
Z
RminusZ
40 50
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Causal relationships between system elements
Stocks and flows
Feedback loop as unit of analysis
Positive (that is, reinforcing feedback) and
Negative (that is, counterbalancing or goalseeking feedback)
Differential equations
Pattern of complex / nonlinear behavior rooted in system structure - endogenous behavior
Hans J. (Jochen) Scholl Slide 14

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Overlapping fields of study
– Economics, ecology, biology, anthropology, psychology, sociology, economics, traffic simulations, military, model testing
– Tragedy of the commons,deer management, predator/prey, beer game
ABM
– Inductive / generative
– Individual / rule based
– Emergent system behavior
– More than one unique set of agents/rules could lead to similar emergent behavior
– Path from emergent behavior down to agent/rule level can be difficult
SDM
– Deductive / analytical
– Aggregate
– Leverage / intervention points
– Causal relationships debatable - expert consensus
MIT Field Trip, 4/20//2001 Hans J. (Jochen) Scholl Slide 15

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According to Phelan, almost identical meaning and usage of terms such as system, emergence, dynamic, nonlinear, adaptive and hierarchy .
Both theories also share a belief that there are universal principles underlying the behavior of all systems.
“Confirmatory analysis” and “problem solving perspective”,
“generating shared understanding and consensus one requires to improve the system” (SDM) as opposed to mainly exploratory research
(ABM).
Individual versus aggregate
MIT Field Trip, 4/20//2001 Hans J. (Jochen) Scholl Slide 16

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ABM modelers typically take positivist to extreme positivist positions
SDM modelers range from positivist to constructivist positions
However, as seen earlier, positivist positions suffered from a serious attack from within (deterministic chaos)
MIT Field Trip, 4/20//2001 Hans J. (Jochen) Scholl Slide 17

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John H. Miller’s ANTs (Active, nonlinear tests)
– A simulation model is subjected to automated testing by use of hillclimbing and genetic algorithms (mutation and crossover). Vast test spaces result
– Sensitivity of a model’s variables to parameter changes in very wide test spaces (zillions of solutions) is uncovered which leads to a better assessment of model validity
– World3 as case in point
MIT Field Trip, 4/20//2001 Hans J. (Jochen) Scholl Slide 18

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SD treatment
– Forrester, Industrial Dynamics (1958)
• Multi-echelon supply chain are “by virtue of policies, organization, and delays…naturally oscillatory”
• Remedies
– Faster order handling
– Better information along the chain regarding consumer demand
– Modest and gradual inventory adjustments
– Sterman, Misperceptions of Feedback (1989a & b)
• Beer distribution game experiment
• Simulation of an economy
– Peter Senge, The Fifth Discipline (1990)
• No strategy - pass on order rule
MIT Field Trip, 4/20//2001 Hans J. (Jochen) Scholl Slide 19

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Economics / Traditional Management Science Treatment
– Lee et al, (1997)
• Distorted information as cause and “rational decision-making”
– Demand signaling
– Order batching
– Price fluctuations
– Rationing and shortage gaming
• Recommended remedies: (1) information sharing, (2) channel alignment, (3) improved operational efficiency
• Strategic interaction among rational supply chain members
MIT Field Trip, 4/20//2001 Hans J. (Jochen) Scholl Slide 20

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Agent-based Modeling Treatment
– Kimbrough et al, (2001)
• ABM implementation of the MIT (stationary input) and the Columbia (stochastic inputs) beer games
• Algorithm
Initialization. A certain number of rules are randomly generated to form generation 0.
2) Pick the first rule from the current generation.
3) Agents play the beer game according to their current rules.
4) Repeat step 3, until the game period (say 35 weeks) is finished.
5) Calculate the total average cost for the whole team and assign fitness value to the current rule.
6) Pick the next rule from the current generation and repeat step 2, 3, and 4 until the performance of all the rules in the current generation have been evaluated.
7) Use genetic algorithms to generate a new generation of rules and repeat steps 2 to 6 until the maximum number of generations is reached
• Experiments
– Mimicking the MIT game -> pass-order rule found - Nash equilibrium
– Stochastic demand input -> agents find better rules than pass-order - no bullwhip effect
– Stochastic demand and stochastic lead time -> agents stiff find better rules and again - no bullwhip effect
MIT Field Trip, 4/20//2001 Hans J. (Jochen) Scholl Slide 21

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Areas of Agreement and Departure
– Traditional research claims the rationality of (local) decision-making
• March & Simon (1958) discussed the demands of such a proposition: (1) all alternatives of choice are given; (2) all consequences are known under certainty, risk, and uncertainty;
(3) rational man has complete utility-ordering
• Lee’s et al definition of rationality is not specified
• Evidence for the lack of local rationality mounting (Sterman, Moxnes)
– No disagreement on other areas (demand inflation, amplification, etc.)
– ABM literature is silent about the causes of the bullwhip effect
– All three strands agree on the remedies (except changing the mental models as proposed by SD)
– ABM demonstrates the existence of better than pass-order solutions
– ABM also confirms the “perceptional source” of the bullwhip effect
MIT Field Trip, 4/20//2001 Hans J. (Jochen) Scholl Slide 22

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Certain similarities despite differences in basic concepts and understandings
Dynamic modeling techniques may have a capacity to complement each other (as in the case of the beer game)
Study of findings and research designs
Understanding strengths and limitations of each modeling technique
Potential for triangulation
“Basic problem” cross study will continue
– Predator/prey
– Deer management
– Tragedy of the commons
Integrated research designs
MIT Field Trip, 4/20//2001 Hans J. (Jochen) Scholl Slide 23

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According to MIS Quarterly (1997) the second most important conference in its field (after ICIS, and before IFIP, DSS, …, Cad.of Mgmt etc.)
This year over 650 attendees (40 percent of whom came from overseas)
85 percent of attendees present papers
Nine main tracks, dozens of minitracks
The 35th conference will be held next January, 7-10
MIT Field Trip, 4/20//2001 Hans J. (Jochen) Scholl Slide 24

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– Over fifty percent of presented papers become journal articles of monographs
– Complex Systems
– Decision Technologies for Management
MIT Field Trip, 4/20//2001 Hans J. (Jochen) Scholl Slide 25

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Minitrack “Modeling Nonlinear Human and Natural Systems”
(formerly
Agent-based Modeling and System Dynamics
) - the minitrack invites papers
– From an SD perspective
– From an Agent-based modeling perspective
– From other nonlinear modeling disciplines
– Highly welcome are papers which incorporate various modeling techniques
MIT Field Trip, 4/20//2001 Hans J. (Jochen) Scholl Slide 26