Organizations must often solve problems with insufficient knowledge. A solver can specialize in a narrow area of expertise or become a generalist to gain a broader understanding of the problem. Despite this well-recognized knowledge structure difference between generalists and specialists, little is known about the impact of in-depth knowledge and wide-ranging knowledge on problem-solving performance. This is due to a lack of understanding of the distinction between knowledge depth and breadth, assuming that deepening knowledge implies accumulating outcomes within one domain, as opposed to broadening knowledge implying more search areas. In this paper, we model generalists and specialists using knowledge depth and breadth, which alters their search behaviors on a multi-level $NK$ fitness landscape. We propose that generalists and specialists structurally have different knowledge structures, which affords them to have different problem-solving capabilities and consequently will contribute solutions differently when collaborating. Specialists can perceive finer granularity and provide local refinement, while generalists can perceive more interdependencies and provide broad repositioning. Having these different perception inclinations, specialists and generalists require different organizing strategies. Specialists may excel at independent search, but generalists benefit more from collective search, especially when communicating with specialists. Problem complexity and knowledge overlap could even complicate these findings. These insights advance our understanding of how we might best coordinate generalists and specialists within organizations.

1. Generalists and Specialists in Collaboration
An Agent-Based Simulation Model
Junyi Li
January 9, 2023
Department of Information Systems and Analytics
National University of Singapore

2. Outline
1. Introduction
2. Theoretical Foundations
3. Simulation Model
4. Results
5. Conclusion
1 / 21

3. Introduction

4. Context
1. Solving problems with insufficient knowledge is ubiquitous in organizations.
2. Preference between knowledge scope and knowledge depth.
• Generalist
• Specialist
2 / 21

5. Research Gap
Empirical evidence highlighting the superior performance of both specialists and
generalists suggests that each has its own advantages and limitations that make
it easier for them to cope with different circumstances [6, 8, 7, 3, 17].
1. Task Requirements [2]
2. Performance Measurement [7]
3. Complexity [16, 1]
However, little is known about why generalists and specialists perform differently,
namely the behavioral differences they exhibit.
3 / 21

6. Research Question
How do generalists and specialists search differently?
4 / 21

7. Research Question
Individually, how do generalists and specialists search differently?
5 / 21

8. Research Question
Collectively, how do generalists and specialists search differently?
6 / 21

9. Theoretical Foundations

10. Problem-Solving as Landscape Search
The rugged landscape metaphor of the NK fitness landscapes model [9] has been
a useful theoretical and analytical instrument to model complex problem-solving
processes.
Conceptualizing problem-solving as a system of actions, we assume that an
individual has to make decisions concerning N actions to generate one solution.
A performance landscape thus is a mapping of any possible set of solutions of
individual actions d =< d1, d2, . . . , dN > to performance values F(d).
However, prior computational models [10, 14, 15] implicitly assume that
collaborating solvers deal with problems at the same level of granularity.
7 / 21

11. Problem-Solving as Landscape Search
We extend the traditional binary NK fitness landscape model toward a
multi-state one.
8 / 21

12. Cognitive Representation in Problem-Solving
1. A simplified cognitive representation [13, 5, 4], although crude, is useful for
examining experiential search in complex environments.
2. Because of the lack of knowledge, solvers can only perceive and propose
solutions from a cognitively simplified landscape [5], to which penalties of
uncertainty are attached.
3. Average pooling algorithms [11, 5, 12] are used to model cognitive
ambiguity such that the average of all alternatives in the refined space is
used to calculate the payoff of a coarse solution.
9 / 21

13. Simulation Model

14. Multi-State Landscape Model
10 / 21

15. Cognitive Representation
11 / 21

16. Local Search
12 / 21

17. Collaboration
For simplicity, we consider a two-member team. To collaborate, the agents need
to align their cognition. We adopt four collaboration patterns proposed by
Gavetti [4]—autonomy, coordination, cognitive control, and circulation of
cognition
13 / 21

18. Collaboration
1. Autonomy
• No Alignment
• Imitate the unknown domains from teammates’ solution
2. Coordination
• Partial Alignment
• Imitate elements based on self-evaluation
3. Cognitive Control
• Mutual Alignment
• Anyone can visit and revise the elements
4. Cognitive Circulation
• Directed Alignment
• One can circulate elements onto the other
14 / 21

19. Results

20. Independent Search
A narrow but refined solution is better than a broad but coarse one when agents
with insufficient knowledge are dealing with the problem.
15 / 21

21. Sequential Search
1. For generalists: under low complexity, G benefits from collaboration with G.
Under high complexity, G benefits from collaboration with S.
2. For specialists: under conditions of low complexity, specialists benefit from
collaboration with G/S.
16 / 21

22. Collective Search: Autonomy
Solvers cannot benefit from the autonomy pattern.
17 / 21

23. Collective Search: Coordination
1. Generalists can benefit from collaboration, especially when collaborating
with specialists. This is because generalists’ unknown domains may be
refined by the specialists and adopted by generalists.
2. In the case of specialists, such complementary knowledge cannot be observed
18 / 21

24. Collective Search: Cognitive Control
The mutually imposed, instead of self-selected alignment undermines not only
the collaboration efficiency but also what could have been achieved without
heavy-handed interventions. (premature convergence)
19 / 21

25. Collective Search: Circulation
1. Generalists benefit from recombining the existing element circulated by
specialists.
2. Specialists lose their advantage in refining solutions since the finest solutions
could be easily replaced by coarse ones.
20 / 21

26. Conclusion

27. Conclusion
Knowledge Structure Problem-Solving Capability Solution Contribution
Generalist Broad yet shallow knowledge Bias toward interdependency percep-
tion;
Broad repositioning
Specialist Narrow yet deep knowledge Bias toward subtlety perception Local refinement
21 / 21

28. Questions?
Outline

29. Key Parameters
N = 12, K varies from 0 to 11, agent number n = 100, landscape repetition is
500. Generalists are modeled as Kb = 6, Kd = 1, while specialists are modeled as
Kb = 3, Kd = 2. N = 12 is preferred because two generalists each specializing in
6 domains would require 12 domains in total if the two generalists did not
overlap in their expertise, as one of the possible cases.

30. Binary Landscapes
1. An individual has to make decisions concerning N actions to generate one
solution. A performance landscape thus is a mapping of any possible set of
solutions of individual actions d =< d1, d2, . . . , dN > to performance values
F(d).
2. Each element di makes a fitness contribution ci to the overall fitness F(d).
3. the fitness contribution ci for ith design element can be represented as
ci = ci(di|K other dj’s). The overall fitness value F(d) is the average of all
fitness contributions, that is, F(d) = 1
N
PN
i=1 ci.

31. Multi-State Landscape
The fitness contribution of the coarse granularity is calculated by taking an
average of the finest alternatives. For example, a fitness contribution of state A
is calculated as the average of two alternatives 1
: state 0 and 1, that is,
ci(di = A) =
ci(di=0|K other dj’s)+ci(di=1|K other dj’s)
2
1Average pooling is well-adopted in the literature [11, 5, 12].

32. Contingency of Knowledge Overlap

33. Contingency of Knowledge Overlap

34. Contingency of Knowledge Overlap

35. Contingency of Knowledge Overlap

36. References

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41. References v
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43. References vii
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