Skip to main content

Table 2 Model settings varied across the three experiments

From: Intelligent judgements over health risks in a spatial agent-based model

Model settings

Exp1

Exp2

Exp3

Threat appraisal

Initial weightsa

 Me, VP, HH, M, CNH

 Weights during a simulation

 Outcome

None

n.a.

n.a.

n.a.

BN1

(0.1; 0.2; 0.01; 0.01; 0.2)

Change as agents learn

RP, (0;1)

BN1

(0.1; 0.2; 0.01; 0.01; 0.2)

Change as agents learn

RP, (0;1)

Coping appraisal

Initial weights

 I,  E, OE, NE

 Weights during a simulation

 Outcome

None

n.a.

n.a.

D1

Deterministic

Rule based, Table 3

Static

D1-D4: fixed population share

BN2

(0.52; 0.74; 0.9; 0.6)

Change as agents learn

D1–D4: adaptive, based on previous experience

  1. aTo elicit the factors that may play a role in the context of a water-spread disease in a developing country as well as their relative importance we ran a survey among students. We approached the participants of the Massive Open Online Course (MOOC) on GeoHealth run at ITC (authors host institute) in Sep, 2016. Majority of the participants of this course are from developing countries. Ideally, one would survey real citizens in the case-study area. This was not possible due to the lack of funds and access to the potential respondents