2024-04
Psychological Safety and Experimentation:

Agent-Based Modeling Approach
Manabu Miyao

Psychological Safety and Experimentation:
Agent-Based Modeling Approach
Manabu Miyao, Kobe University
Introduction
Experimentation is an essential activity for contemporary firms intending continuous
innovation and growth (Bojovic et al., 2018; Felin et al., 2020; Thomke, 2003).
Experimentation is defined as “the use of models, prototypes, controlled environments,
and computer simulations that allow innovators to reflect, improvise, and evaluate themany ideas generated in organizations” (Thomke, 2003, p. 6). Experimentation varies
across industries, but it shares a commonality of trial and error learning. In particular,
collaborative innovation requires experimentation to test and evaluate ideas many
participants bring in (Chesbrough, 2010; Hartley et al., 2013).
Given the important role of experimentation in continuous and collaborative
innovation, managers should build an organizational environment that facilitates
employees' experimentation. In these environmental factors, practitioners and scholars
have recently focused on psychological safety (Cannon & Edmondson, 2005; Thomke,2020). Psychological safety is a shared belief within a team that the team is safe forinterpersonal risk-taking (Edmondson, 1999). As one cannot foresee the result or avoid
potential experimentation failure, employees are less likely to experiment if they believe
they are unsafe for taking risks. Conversely, employees are more likely to experiment ina climate that allows taking risks (Naranjo-Valencia et al., 2017).
However, when we focus on the individual's experimentation and its consequences,
we can see a dynamic relationship between psychological safety and employees'
experimentation (Coutifaris & Grant, 2022). For example, if one receives a penalty for
his/her failure in experimentation, he/she will feel less safe about taking risks and, hence,less likely to experiment at the next opportunity. This causal relationship between
experimentation and demotivation is less examined. How does psychological safetyemerge from the micro dynamics of experimentation and its consequences?

Research design
To investigate this research question, we develop the agent-based model to simulate
individuals' experimentation and its consequences within an organization. The agentbased model is a method for studying systems composed of interacting agents (Secchi &Neumann, 2016). One deploys agents in the virtual space, defines interaction rules for theagents, and runs simulations under various conditions. This method can examine factorsthat are difficult to control within real organizations and thus is appropriate for the aim of
this study.
This study develops an agent-based experimentation model based on the garbage
can model (Cohen et al., 1972; Fioretti & Lomi, 2008; Herath et al., 2016). Our modelcomprises four agents: employee, idea, apparatus, and opportunity. The employee agents
have two parameters: ability and motivation. The idea and apparatus agents have one
parameter: difficulty and efficacy, respectively. These four agents move randomly andinteract in the virtual space, representing an organization. When the four agents gather inthe space, the employee tests the idea with the available apparatus at the opportunity.
Then, the experimentation succeeds if the following condition is satisfied:
E(a) × A(e) ≥ I(d)(1)

where E(a), A(e), and I(d) denote the employee's ability, the apparatus's efficacy, and theidea's difficulty, respectively.
If condition (1) is not satisfied, the experimentation fails. Depending on theparameters set, the employee may learn from failure (his/her ability rises) or receive apenalty (his/her motivation drops).
When an employee, an idea, and an opportunity gather, the employee decides
whether to hold or discard the idea. The decision depends on the employee's motivation;
if the motivation is lower than the threshold, the employee discards the idea.
The simulation is composed of 100 of each agent type. At each simulation step, theagents randomly move within the organization and make decisions when the other agents
gather around an employee. After 1,000 steps, the simulation is completed. Table 1 showsthe parameters given in the simulation.
Table. 1 Parameter of the simulation
Parameter

Value

Number of agents:
Employee

100

Idea

100

Apparatus

100

Opportunity

100

Initial motivation of employee

N(50, 100)

Initial ability of employee

N(50, 100)

Efficacy of apparatus

N(0.8, 0.2)

Threshold motivation to discard idea

40

Learning from failure (LFF){0, 20}Penalty{0, 20}Result and discussion
Table 2 shows the results of the simulations. We ran simulations twelve times in eachsetting and calculated means by removing the data with the most and least experiments.
The comparison between settings 1 and 2 revealed that employees' motivation decreased
when receiving a penalty, and hence, employees discarded more ideas and lessexperimented. The comparison between settings 1 and 3 also showed that the success raterose when employees could learn from failure. Moreover, setting 4 resulted in a highsuccess rate, but innovation, the sum of succeeded ideas' difficulty, was lower than setting
3. This implies that a penalty does not impact the success rate but reduces innovation
because the penalty increases idea discarding.
These results provide two theoretical implications to the psychological safety andexperimentation literature. First, psychological safety in an organization can beconceptualized as the presence or absence of a penalty for failure and the resulting
demotivation. Second, the suppression of experimentation due to low psychologicalsafety can be conceptualized as increased idea-discarding due to decreased motivation.
Moreover, our results provide practical implications; a penalty appears to increase thesuccess rate but inhibits experimentation and discourages innovation. Blaming failure
harms experimentation and innovation.
Table 2 Setting and result of the simulation
Setting

Resulta

No Penalty LFF

Experi

Success Discarded Mean

mentation
1234020020002020

Mean

ability motivation

Success Innovationbrate

59.9

12.1

34.450.3

50.6

20.4%

513.1

(1.6)

(0.9)

(1.8)

(0.3)

(0.3)

(1.6)

(40.7)

46.4

10.1

51.2

50.2

43.3

21.8%

418.9

(5.3)

(2.6)

(5.5)

(1.0)

(0.7)

(5.7)

(112.5)

59.1

17.4

35.7

58.7

50.8

29.6%

749.7

(1.6)

(0.8)

(1.5)

(0.3)

(0.3)

(1.4)

(28.2)

48.3

16.4

48.9

57.5

44.5

34.0%

677.0

(0.7)

(1.4)

(0.9)

(0.4)

(0.5)

(2.9)

(56.4)a

Mean and standard error (n=10)b

Sum of succeeded ideas' difficultyReferences
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