Optimizing Campaigns for Changing Routine Behaviors by Using an Empirically Calibrated Microsimulation Model

Bibliographic Data

ID12170453
AuthorsRobert Tobias (0000-0001-7972-122X, Eawag, Dübendorf, Switzerland, corresponding author), Hans-Joachim Mosler (0000-0002-3532-6487, Eawag, Dübendorf, Switzerland)
Year2015
Volume35
Issue2
Pages184-202
Publication date2015-12-24
Open AccessYes
TypeARTICLE
VenueSocial Science Computer Review (JOURNAL)
Journal identifiersISSN: 0894-4393 • E-ISSN: 1552-8286
PublisherSAGE Publishing (PUBLISHER • US)
DOI10.1177/0894439315620866
OpenAlexW2407712456
LanguageEN
References cited24

We used the model of prospective memory and habit development to derive recommendations for designing behavior-change campaigns that used prompts or household visits as reminders. We followed an exemplary procedure comprising the calibration of the model, based on 48 time series gathered during a campaign promoting recycling habits and a systematic exploration of the solution space. For the parameter estimation, an algorithm was developed that worked at two levels. A higher level algorithm optimized parameters that were set to equal values for all agents, whereas a lower level algorithm estimated the values of agent-specific parameters for each agent separately, using the parameter values of the higher level algorithm for the other parameters. This procedure resulted in an excellent fit of the model to the data ( R 2 = 75%) For the systematic exploration, an indicator expressing campaign effects in one value was defined and the following findings could be derived. Activities should focus on the first week of a campaign. Follow-up visits or refreshing of prompts should be done within 4 days after the initial visit. Later activities, such as additional visits or refreshing of prompts, bring little further effects. Investing heavily in the design of the prompts for improving their salience is only worthwhile in populations with a low commitment to perform the behavior. Furthermore, covering more than 10% of the places where the target behavior should be performed with prompts mostly does not lead to additional effects to make it worthwhile

Calibration · Machine learning · Microsimulation · Operations research · Salience (neuroscience · Set (abstract data type · Statistics · Transport engineering · Value (mathematics · Behavioral Health and Interventions · Cognitive Functions and Memory · Computer Science · Engineering · Mathematics · Psychology · Technology Use by Older Adults · Artificial Intelligence

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