Shinsuke Sakuma (Waseda Univ.), Yusuke Goto (Iwate Pref. Univ.), and Shingo Takahashi (Waseda Univ.) Analysis of Knowledge Retrieval Heuristics Considering Member's Load Balancing The 3rd World Congress on Social Simulation September 9, 2010 (Kassel, Germany)

1. WCSS 2010 Thursday, September 9, 2010
University of Kassel, Kassel, Germany
Analysis of Knowledge Retrieval Heuristics
Considering Member s Load Balancing
Sakuma, Shinsuke Nomura Research Insutitute
Goto, Yusuke
Iwate Prefectural University
Takahashi, Shingo Waseda University
1

2. Introduction
Theme:
Knowledge management & Knowledge retrieval
Target situation:
•Knowledge workers are highly specialized
•Working collaboratively
Ex. project team of consulting firm,
R&D department, university laboratory ...
Target problem:
•Effective knowledge sharing within a work group
•Effective knowledge retrieval within a work group
2

3. Knowing who knows what
Wegner & Wegner (1995):
Transactive Memory (TM):
meta-knowledge for effective knowledge retrieval
Faraj & Sproull (2000), Hollingshead (1998):
Knowing who knows what has a positive impact
on a work group performance
Why?
Basden et al. (1997):
•Knowing who knows what
has a negative impact
on a work group performance
•Knowing who knows what causes
a retrieval disruption
3

4. ABSS experiments
Summary of experiments:
Inuzuka &
Nakamori
(2003)
Ren et al.
(2006)
Sakuma
et al.
(2010)
This study
Size of group
Bounded rationality
of TM
Heuristics of
knowledge retrieval
Load balancing
among knowledge
workers
Is the retrieval disruption caused by
a concentration of request queries?
4

5. Objective & method
Objective:
To examine how the load of co-workers (respondents)
affects the effectiveness of knowledge retrieval
Research question:
•When does a heavy concentration of queries occur?
•What types of knowledge retrieval heuristics
cause (or prevent) such a heavy concentration of
queries?
Method:
1.Building a model that considers the request load of
respondents in the knowledge retrieval process
2.Verification and validation of the model
3.ABSS experiment
5

6. Model outline & flowchart
Outline:
•Knowledge workers refer to their TM
and retrieve required knowledge from co-workers
in order to execute assigned tasks.
•Abstract model (or middle-range model)
Flowchart:
Start
Assign task j to agent i
Does i have sufficient
knowledge to execute j?
Yes
No
Knowledge retrieval process
i executes j
Has i executed all tasks?
No
Yes
End
6

7. TM & learning
Transactive Memory (TM):
Knowledge worker i s TM that co-worker j seems to
have knowledge k.
tmijk
1 if i deems that j does not have k ,
= 0 if i lacks information about whether j has k or not,
1 if i deems that j has k
* i s own knowledge is precisely recognized
Learning:
TM is updated as a result of communications
during the knowledge retrieval process
respondent
requester
-1
0
1
-1
-1
-1
0
0
-1
0
1
1
0
1
1
7

8. Knowledge retrieval heuristics
Retrieval heuristics:
Referring their own TM about who knows what ,
knowledge workers determine whom to ask for what.
Ask to whom:
who seems to know the required knowledge and ...
(1)has the most knowledge
(2)has the least knowledge
(3)who is randomly selected
Ask for what:
(a)the required knowledge
(b)respondent s knowledge (know-what)
(c)respondent s know-who
(who knows the required knowledge)
(d)respondent s TM (who-knows-what)
8

9. Six types of heuristics
I. Straight
II. Know-what
III. Know-who
IV. Who-knows-what
V. Random
VI. On my own
Start
Ask to whom?
V
(3)
(1) or (2)
III
(c)
Acquire the required
knowledge on his/her own
I
Ask what?
II
(b)
VI
IV
(d)
Meta-knowledge
(1) the most knowledgeable
(2) the least knowledgeable
(3) random
(a)
Knowledge
End
(a) the required knowledge
(b) respondent s knowledge (know-what)
(c) respondent s know-who
(who knows the required knowledge)
(d) respondent s TM (who-knows-what) 9

10. Time cost & performance indices
Time cost:
(1)
queue Tshare
requester
respondent
(2)
Town
learn the required
knowledge on my own
We tentatively set Tshare = 1.00 and Town = 7.74
on the basis of Inzuka & Nakamori s result
Performance indices:
(1)Time:
average time cost until knowledge workers
accomplishes all assigned tasks
(2)Accuracy:
average success probability of knowledge
workers acquiring
10

11. Verification & Validation (V&V)
Verification:
Confirming whether an implemented model matches
its conceptual specification.
Log tracing of ABSS and consistency test
with the output calculated manually
Validation:
For abstract models, confirming whether the model s
output is consistent with the known propositions
about the target problem.
(1)With regard to making decisions, the use of
TM helps small groups to a greater extent than
it helps large groups.
(2)With regard to the speed of processing tasks,
the use of TM helps large groups to a greater
extent than it helps small groups.
11

12. Experimental design
Aim:
•When does a heavy concentration of queries occur?
•What types of knowledge retrieval heuristics
cause (or prevent) such a heavy concentration of
queries?
Base scenario:
•A group leader (GL) has all types of knowledge
•General members (GM) have 12 types of knowledge
•Ask to whom seems to know the required knowledge
and has the most knowledge
Agents
Types of
knowledge
Assigned tasks
Average required
knowledge
Simulation runs
40
240
50
4.8
100
Dni = 0.9
Degree of existence
of initial recognition in TM
Acci = 0.9
Accuracy of initial TM
12

13. Base scenario
I
II
III
IV
V
VI
Straight
Knowwhat
Knowwho
Whoknowswhat
Random
On my
own
avg.
1347.0
1367.5
1358.3
1412.3
502.6
1092.8
σ
24.1
27.2
21.2
25.8
6.3
8.4
avg.
0.93
0.94
0.93
0.94
0.54
1.00
GL
4585.2
4610.4
4584.6
4655.6
560.4
N.A.
GMs
51.3
48.0
49.5
44.3
549.7
N.A.
Time
Accuracy
Query
13

14. Heuristics change
I
II
III
IV
V
VI
Straight
Knowwhat
Knowwho
Whoknowswhat
Random
On my
own
avg.
370.4
368.9
369.7
357.4
503.0
1092.8
σ
3.6
3.9
3.0
4.5
6.1
8.4
avg.
0.74
0.74
0.74
0.78
0.54
1.00
GL
87.0
78.3
87.4
90.2
561.9
N.A.
GMs
241.6
238.0
240.0
222.5
551.0
N.A.
Time
Accuracy
Query
14

15. Work group size change
I
II
III
IV
V
VI
Straight
Knowwhat
Knowwho
Whoknowswhat
Random
On my
own
avg.
281.0
286.5
282.6
292.3
231.5
773.2
σ
8.5
9.7
7.8
10.6
6.0
15.7
avg.
0.98
0.99
0.99
0.99
0.76
1.00
GL
654.8
669.5
654.4
686.3
218.0
N.A.
GMs
25.6
21.2
24.3
18.9
185.9
N.A.
Time
Accuracy
Query
15

16. TM indices change
I
II
III
IV
V
VI
Straight
Knowwhat
Knowwho
Whoknowswhat
Random
On my
own
avg.
477.5
539.2
464.8
648.5
502.7
1092.3
σ
5.9
74.0
29.5
87.8
5.5
9.1
avg.
0.59
0.82
0.59
0.77
0.54
1.00
GL
1228.7
2506.2
1234.2
3162.5
561.4
N.A.
GMs
472.5
212.0
443.6
240.8
550.6
N.A.
Time
Accuracy
Query
16

17. Analysis of the simulation result
Effect of group size & TM indices:
•Group size have no impact on the query concentration.
•TM indices have an impact on the query concentration.
I
Base scenario GL
II
III
IV
V
VI
4585.2
4610.4
4584.6
4655.6
560.4
N.A.
Group size
GL
654.8
669.5
654.4
686.3
218.0
N.A.
TM indices
GL
1228.7
2506.2
1234.2
3162.5
561.4
N.A.
Effect of knowledge retrieval heuristics:
Modification of heuristics achieves request load balancing.
I
II
III
IV
V
VI
Query by (1)
GL
4585.2
4610.4
4584.6
4655.6
560.4
N.A.
Query by (2)
GL
87.0
78.3
87.4
90.2
561.9
N.A.
17

18. Summary
Working hypotheses:
1.Regardless of the size of the work group, a heavy
concentration of the request load would occur when
there is an outstanding knowledgeable worker in the
work group, the workers know each other well, and the
workers ask the co-worker who seems to have the
required knowledge and the most knowledge.
2.For balancing the load o respondents and for effective
knowledge retrieval, the knowledge workers should use
TM and ask the co-worker who seems to have the required
knowledge, but the least knowledge.
Summary:
• Building a model that considers the request load of
•
respondents in the knowledge retrieval process
Two working hypotheses for further research
18

19. Future study
Theory development:
• Formulate some hypotheses on the effective
•
knowledge retrieval process
Need for further experiments under various types of
work group conditions
Applications of the model:
• Tackle a specific problem situation in the real world
•
Ex. project team of consulting firm, R&D department,
university laboratory ...
Need for further calibration of our model &
parameters to address the target problem.
19

20. References
1.Faraj, S., Sproull, L.: Coordinating Expertise in Software Development
Teams. Management Science 46(12) 1554‒1568, 2000
2.Wegner, T. G., Wegner, D. M.: Transactive Memory. In: Manstead, A. S. R.,
Hewstone, M. (eds.) The Blackwell Encyclopedia of Social Psychology,
1995
3.Hollingshead, A. B.: Communication, Learning, and Retrieval in
Transactive Memory Systems. Journal of Experimental Social Psychology
34: 423‒442, 1998
4.Basden, B. H., Basden, D. R., Thomas III, R. L.: A Comparison of Group and
Individual Remembering Does Collaboration Disrupt Retrieval Strategies?
Journal of Experimental Psychology: Learning, Memory, and Cognition 23
(5) 1176‒1189, 1997
5.Ren, Y., Carley, K. M., Argote, L.: The Contingent Effects of Transactive
Memory: When is It More Beneficial to Know What Others Know?
Management Science 52(5) 671‒682, 2006
6.Sakuma, S., Goto, Y., Takahashi, S.: Analysis of Knowledge Retrieval
Heuristics in Concurrent Software Development Teams. In: Takadama, K.,
Revilla, C. C., Deffuant, G. (eds.): The Second World Congress on Social
Simulation. Springer, Forthcoming
7.Inuzuka, A., Nakamori, Y.: A Recommendation for IT-Driven Knowledge
Sharing. The Transactions of the Institute of Electronics, Information and
Communication Engineers. J86-D-I: 179‒187, 2003 (in Japanese)
8.Gilbert, N.: Agent-Based Models. Sage Publications, 2007
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