CAS Symposium (Oct 12 2013)

developing information systems as complex adaptive business systems

jungpil hahnnational university of singapore

complexity in ISD

• technical complexity

• organizational complexity

essence of ISD complexity

• poorly understood requirements

• changing requirements

• multiplicity of perspectives, objectives, incentives

an impossible task?

• impossible to anticipate / predict

• all possible types of changes to business environments

• all possible types of changes to new developments in information technologies

• all possible configurations of organizational tensions

how should we (re)conceptualize IS development in light of inherent complexity?

a novel conceptualization of ISD

• ISD as goal-driven discovery process

• characterized by simple rules of adaptation

• within a complex environment characterized by complexity, uncertainty and dynamism

• shift in focus

• from: designing response to specific complexity features

• to: devising general discovery routines that perform well under a variety of conditions that represent complex ISD requirements environments

a model of ISD as landscape search/discovery

NK fitness landscape model

the basic NK model

• system of interdependent choices

• N decisions about how to configure a system

• each decision contributes to the fitness of the system;

• fitness contribution of decision i (0..N) depends also on how K other decisions are resolved

• discovery via local search

d = <d1, d2, ..., dN>

c(di) = ci(di; K other djs)

change di if fitness improves

Smooth Landscape Rugged Landscape

K K

landscape search

dcurrent = <0,1,0,0,1,...,0,1,1,0>

doptimal = <1,1,0,1,1,...,1,0,1,1>

landscape search

dt-1 = <0,1,0,0,1,...,0,1,1,0> dcurrent = <1,1,0,0,1,...,0,1,1,0>

doptimal = <1,1,0,1,1,...,1,0,1,1>

landscape search

dt-1 = <1,1,0,0,1,...,0,1,1,0> dcurrent = <1,1,0,0,1,...,0,1,1,1>

doptimal = <1,1,0,1,1,...,1,0,1,1>

landscape search

dt-1 = <1,1,0,0,1,...,0,1,1,1> dcurrent = <1,1,0,0,1,...,1,1,1,1>

doptimal = <1,1,0,1,1,...,1,0,1,1>

landscape search

dt-1 = <1,1,0,0,1,...,1,1,1,1> dcurrent = <1,1,0,1,1,...,1,1,1,1>

doptimal = <1,1,0,1,1,...,1,0,1,1>

landscape search

dt-1 = <1,1,0,1,1,...,1,1,1,1> dcurrent = <1,1,0,1,1,...,1,1,1,1>

doptimal = <1,1,0,1,1,...,1,0,1,1>

landscape search

dt-1 = <1,1,0,1,1,...,1,1,1,1> dcurrent = <1,1,0,1,1,...,1,0,1,1>

= doptimal

doptimal=<1,1,0,1,1,...,1,0,1,1>

rugged landscape search

dcurrent = <0,1,0,0,1,...,0,1,1,0>

doptimal = <1,1,0,1,1,...,1,0,1,1>


dt-1 = <0,1,0,0,1,...,0,1,1,0> dcurrent = <0,1,0,0,0,...,0,1,1,0>

doptimal = <1,1,0,1,1,...,1,0,1,1>


dt-1 = <0,1,0,0,0,...,0,1,1,0> dcurrent = <1,1,0,0,0,...,0,1,1,0>

doptimal = <1,1,0,1,1,...,1,0,1,1>


dt-1 = <1,1,0,0,0,...,0,1,1,0> dcurrent = <1,1,0,1,0,...,0,1,1,0>

doptimal = <1,1,0,1,1,...,1,0,1,1>


doptimal = <1,1,0,1,1,...,1,0,1,1>

dt-1 = <1,1,0,1,0,...,0,1,1,0> dcurrent = <1,1,0,1,0,...,1,1,1,0>

suboptimal local peak

ISD is conceptualized as ...

• a goal-directed problem solving process

• whereby an ISD organization designs and develops an information system

• in the context of some requirements environment





elements of a theory of ISD






need to extend the NK fitness landscape model





requirements environment

• complexity

• cognitive complexity

• design complexity

• uncertainty

• dynamism


• complexity



• uncertainty

• dynamism

• number and interdependency among systems design choices

• structure of the design problem

cognitive complexity

low complexity high complexity

K K

design complexity

x x x x! x x x x! x x x x!x x x x! x x x x!x x x x ! x x x x !x x x x! x x x x ! x x x x!

x x x x! x x x x !X x x x!x x x x!x x x x! x x x x! x x x x! x x x x! x x x x ! x x x x!

x x x x! x x x x! x x x x!x x x x!x x x x! x x x x ! x x x x!x x x x! x x x x ! x x x x!

K K K

modular nearly decomposable random


• complexity



• uncertainty

• dynamism

• inability to accurately assess implications of design choices

no uncertainty

low uncertainty

moderate uncertainty

high uncertainty


• complexity



• uncertainty

• dynamism • change in implication of design choices

dynamism

t=0

t=1

dynamism

t=2

dynamism

t=3

dynamism

t=4

dynamism

before (t=0) after (t=4)

dynamism





ISD organization

• differences in knowledge

• differences in incentives

ISD organization


• differences in incentives

• partition the problem space into subsets representing different knowledge domains

knowledge / task partitioning

d = <d1, d2, d3, . . . , dm, dm+1, . . . , dN-1, dN>

knowledge / task partitioning

business unit IS unit

d = <d1, d2, d3, . . . , dm, dm+1, . . . , dN-1, dN>

within domain interdependencies


d = <d1, d2, d3, . . . , dm, dm+1, . . . , dN-1, dN>

within domain interdependencies


d = <d1, d2, d3, . . . , dm, dm+1, . . . , dN-1, dN>

across domain interdependencies


d = <d1, d2, d3, . . . , dm, dm+1, . . . , dN-1, dN>

domain

technology

domain technology

knowledge/task partitioning

x! x! x! x! x! x!

x! x! x! x! x! x!

x! x! x! x! x! x!

x! x! x! x!

x! x! x! x! x!

x! x! x! x! x! x!

x! x! x! x! x! x!

x! x! x! x!

x! x! x! x!

x! x! x! x! x! x!

x!

x!

x!

x!x!

ISD organization


• differences in incentives • weighting of fitness contributions

fitness value

F(d) = ci

i=1

N

∑ N

fitness perception w/ incentives

Fbus' (d) = c

ii=1

m

∑ + incent × ci

i=m+1

N

∑⎛⎝⎜

⎞⎠⎟N

FIS' (d) = incent × c

ii=1

m

∑ + ci

i=m+1

N

∑⎛⎝⎜

⎞⎠⎟N





problem solving process

• partitioning of decisions

• cognitive vs. experiential discovery

• information processing power





• constraining scope of search

search scope

d = <d1, d2, d3, d4, d5, d6,. . . , dN-2, dN-1, dN>

d = <d1, d2, d3, d4, d5, d6,. . . , dN-2, dN-1, dN>

iterative / incremental approach

d = <d1, d2, d3, d4, d5, d6,. . . , dN-2, dN-1, dN>


d = <d1, d2, d3, d4, d5, d6,. . . , dN-2, dN-1, dN>


d = <d1, d2, d3, d4, d5, d6,. . . , dN-2, dN-1, dN>


planned-based approach

d = <d1, d2, d3, d4, d5, d6,. . . , dN-2, dN-1, dN>





• exhaustive vs. trial-and-error search

exhaustive search

d = <1, 0, 0, 1, 1, 1, 0, 1, 0, 0>

d’1 = <0, 0, 0, 1, 1, 1, 0, 1, 0, 0>d’2 = <1, 1, 0, 1, 1, 1, 0, 1, 0, 0>d’3 = <1, 0, 1, 1, 1, 1, 0, 1, 0, 0>d’4 = <1, 0, 0, 0, 1, 1, 0, 1, 0, 0>

adopt d’3 if F(d’3)=max(F(<d’>)) and F(d’3) > F(d)

trial-and-error search

d = <1, 0, 0, 1, 1, 1, 0, 1, 0, 0>

d’r = <1, 0, 0, 0, 1, 1, 0, 1, 0, 0>

adopt d’r if F(d’r) > F(d)




• information processing power • number of alternatives in consideration set

information processing power

d = <1, 0, 0, 1, 1, 1, 0, 1, 0, 0>

d’1 = <0, 0, 0, 1, 1, 1, 0, 1, 0, 0>d’2 = <1, 1, 0, 1, 1, 1, 0, 1, 0, 0>d’3 = <1, 0, 1, 1, 1, 1, 0, 1, 0, 0>d’4 = <1, 0, 0, 0, 1, 1, 0, 1, 0, 0>d’5 = <0, 1, 0, 1, 1, 1, 0, 1, 0, 0>d’6 = <1, 1, 0, 0, 1, 1, 0, 1, 0, 0>










interactions among elements






task partitioning x problem complexity






design complexity x knowledge disparity






iteration scope size x information processing capacity

some applications

understanding the role of shared domain knowledge between domain users and IS personnel on ISD effectiveness

<1>

modeling shared domain knowledge

d = <d1, d2, d3, d4, d5, d6, d7, d8, d9, d10>



d = <d1, d2, d3, d4, d5, d6, d7, d8, d9, d10>

own knowledge overlapping knowledge

sbus = 2



d = <d1, d2, d3, d4, d5, d6, d7, d8, d9, d10>

overlapping knowledge own knowledge

sIS = 3


impact of shared domain knowledge

impact of shared domain knowledge

F(s=8, K=15)

F(s=6, K=15)

F(s=4, K=15)

F(s=2, K=15)

F(s=0, K=5)F(s=2, K=6)F(s=4, K=7)

F(s=6, K=10)

F(s=0, K=7)F(s=2, K=9)

F(s=4, K=11)

F(s=0, K=10)F(s=2, K=12)

F(s=0, K=13)

>

>

>

>

distribution of shared domain knowledge

understanding the impact of iteration scope size on ISD effectiveness

<2>

modeling iteration scope size

d = <d1, d2, d3, d4, d5, d6,. . . , dN-2, dN-1, dN>

d = <d1, d2, d3, d4, d5, d6,. . . , dN-2, dN-1, dN>

iteration scope size (R) = 2, iteration 1

work on module 1 (<d1, d2>)

d = <d1, d2, d3, d4, d5, d6,. . . , dN-2, dN-1, dN>



d = <d1, d2, d3, d4, d5, d6,. . . , dN-2, dN-1, dN>

integrate module 2 into current system (module 1)


d = <d1, d2, d3, d4, d5, d6,. . . , dN-2, dN-1, dN>



d = <d1, d2, d3, d4, d5, d6,. . . , dN-2, dN-1, dN>

integrate module 3 into current system (module 1&2)


d = <d1, d2, d3, d4, d5, d6,. . . , dN-2, dN-1, dN>

work on module N/R-1 (<dN-3, dN-2>)

iteration scope size (R) = 2, iteration N/R-1

d = <d1, d2, d3, d4, d5, d6,. . . , dN-2, dN-1, dN>

integrate module N/R-1 into current system (modules 1..N/R-2)

iteration scope size (R) = 2, iteration N/R-1

d = <d1, d2, d3, d4, d5, d6,. . . , dN-2, dN-1, dN>

work on module R (<dN-1, dN>)

iteration scope size (R) = 2, final iteration

d = <d1, d2, d3, d4, d5, d6,. . . , dN-2, dN-1, dN>

integrate module N/R into current system (modules 1..N/R-1)

iteration scope size (R) = 2, final iteration

impact of iteration scope size and uncertainty on performance

impact of iteration scope size and uncertainty on project duration

conclusions

conclusions

• a novel approach for incorporating sociotechnical complexity into the study of ISD

• computational framework that extends on NK fitness landscape model as analytical approach for studying ISD for complex adaptive business systems

one more thing

http://github.com/jungpil/nkre

experience with complexity perspective

1. how has the adoption of the complexity perspective changed the fundamental assumptions, logic, and methods you use in your research?

2. how do you communicate your research on complexity when the assumptions of both the academic and practitioner target audience are very different than yours?

3. what new research questions can we ask regarding the complex nature of your research phenomena?

Technology

CAS Symposium (Oct 12 2013)