=Paper=
{{Paper
|id=Vol-2297/paper4
|storemode=property
|title=Ethical Challenges in Modeling and Simulation of Nudging in Care
|pdfUrl=https://ceur-ws.org/Vol-2297/paper4.pdf
|volume=Vol-2297
|authors=Stephanie C. Rodermund,Fabian Lorig,Ingo J. Timm
|dblpUrl=https://dblp.org/rec/conf/wirtschaftsinformatik/RodermundLT19
}}
==Ethical Challenges in Modeling and Simulation of Nudging in Care==
https://ceur-ws.org/Vol-2297/paper4.pdf
Ethical Challenges in Modeling and Simulation of
Nudging in Care
Stephanie C. Rodermund1, Fabian Lorig1, Ingo J. Timm1
1 Business Informatics 1, Trier University, 54296 Trier, Germany
{rodermund,lorigf,itimm}@uni-trier.de
Abstract. Due to the demographic change, there is a disproportionately
increasing demand for professional care services in contrast to available
caregivers. Consequently, innovative technologies, e.g., Internet-of-Things
devices or robotics enabling a change of behavior, must be developed to
strengthen patients’ independency for improving patients’ quality of life and for
exonerating the caregivers. Therefore, nudging can be used to initiate beneficial
behavior change. To evaluate the effectiveness of different nudging methods,
modeling and simulation can be used. In this paper, we discuss ethical
implications of such simulations with respect to the conflict of interest between
the individuals’ autonomy, self-determined life, and duty of care. Thereby,
challenges that developers have to face are identified and discussed.
Keywords: Methods of Behavior Change, Nudging, Agent-based Simulation,
Ethical Implications, Health Care
14th International Conference on Wirtschaftsinformatik,
February 24-27, 2019, Siegen, Germany
35
1 Introduction
As a result of the demographic change, the population is ageing and the number of
people in need of care increases. Experts predict no reversal of this trend [1, 2, 3]. This
results in an ascending demand of professional care services, whereas this demand
cannot be met by the number of available caregivers [4]. Hence, the independence of
people in need of care must be preserved for as long as possible. Thereby, a longer care
at home can be enabled because care-dependents are able to execute simple tasks, which
otherwise would afford professional assistance. One approach to achieve a higher
degree of independence is to encourage beneficial behavior, e.g., through using
innovative technologies like Internet-of-Things devices. The concept of nudging
summarizes methods, that are meant to influence behavior without forcing it by taking
away options or setting high financial incentives [5]. Before using a method, its
effectiveness in encouraging the intended behavior should be ensured to prevent
negative consequences. To evaluate the effectiveness of different nudging methods,
modeling and simulation can be used [6, 7]. This paper discusses ethical implications
that infer from such a simulation approach with respect to the conflicts of interest
between the individuals’ autonomy, self-determinism, and duty of care.
This work is structured as follows: Section 2 gives an introduction in the concept of
nudging, as well as modeling of nudging in care. In Section 3, a discussion on the ethical
implications of nudging in care and of a respective simulation model is presented.
Finally, in Section 4 we conclude and discuss potential future work.
2 Modeling and Simulation of Nudging
The concept of nudging, as defined by Thaler and Sunstein, describes methods of
manipulating human behaviors without decreasing the choice set or making use of
prohibitions [5]. Nudging covers methods that change a given decision architecture to
generate behavior that is beneficial for the decision-maker or general public. Methods
of nudging include, e.g., provision of defaults and feedback, or structuring complex
decisions. The approach stems from behavioral economics and is mainly used as
political instrument, e.g., to increase sustainability and energy efficiency [8, 9], to
increase work efficiency of employees [10, 11] or as a political instrument in healthcare
[12, 13, 14]. Furthermore, nudging is already used in nursing and assisted living to
improve the patients’ independence, e.g., to increase urine consistency [15, 16, 17].
Modeling and simulation have established for analyzing and evaluating behaviors
[18, 19]. To measure the effect of nudging methods on care-dependents’ behavior via
simulation, nudges have to be formalized, which means altering the patients’
environmental conditions. Furthermore, the cognitive decision-making of the care-
dependents has to be modeled to represent the processes that cause acceptance and
rejection of nudging methods, and the respective behavior of the care-dependents. To
model care-dependents, Agent-based Modeling (ABM) seems to be well-suited, as it
has established in modeling of cognitive decision-making and behaviors of human
beings [20, 21, 22, 23, 24]. For generating model behavior, empirical studies must be
36
carried out which collect both general data on the patients (e.g. degree of independence)
and on the behavior investigated before and after the addition of specific nudging
methods. An example that can lead to an improvement in independence is to encourage
care-dependents to drink enough. Dehydration can lead to health issues and states of
confusion that decrease the self-sufficiency capacity especially of the elderly [25].
Drinking could be nudged, e.g., by providing feedback on the patients’ behavior or by
reminding them to drink by using IoT devices [26, 27] or motion detectors that are
placed near water sources and provide visual stimuli (light). This scenario serves as an
application example for modeling and simulation.
In the following, we discuss ethical implications that must be addressed while
modeling and simulation in order to contribute to improving the quality of the care-
dependents’ lives.
3 Ethics of Simulating Nudging in Care
First, we consider the ethical implications of nudging in care and then discuss moral
challenges of simulation in this domain.
3.1 Ethics of Nudging in Care
The ethics of nudging offer a broad area for discussion, where the concept is mainly
criticized. The major points of criticism focus on endangering the decision-makers'
autonomy and their growing habituation to manipulation. Furthermore, the
consequences of manipulation are not necessarily suitable for each individual,
dependent on the given context, as well as determined by the intention of the decision
architect [28, 29]. Assistive Technologies (AT) also target maintaining the independence
of those in need of care. Ultimately, the ethical considerations here focus on four well-
established principles formulated by Beauchamp and Childress [30]. These principles
(autonomy, beneficence, non-maleficence, justice) derive and are underpinned from
multiple sources as common morality, health ethics, the Declaration of Helsinki as well
as basic laws (e.g., need for consent before medical procedure execution or Art. 1 of
the Charter of fundamental rights of the European Union) [30, 21, 32, 33].
3.2 Moral Challenges in Modeling and Simulation of Nudging in Care
A simulation model is needed to formalize different nudges and their impacts on care-
dependents’ behaviors. As mentioned earlier, simulation models of nudging and
behavior change already exist [6, 7]. Nevertheless, transferring this to the care domain
confronts us as developers with new challenges. Since empirical studies are required
for model creation, and the results of a simulation are meant to be transferred to reality
where care-dependents are affected, ethical challenges of modeling and simulation in
this context have to be discussed.
Several codes and guidelines have built that shall “inspire and guide the ethical
conduct of all computing professionals” [34]. These guidelines and principles address
37
ethical questions that developers have to face and demand the developer to supply a
positive contribution for society and its environment [34, 35, 36]. Therefore, the
benefits for the patients (increase of independency and quality of life) must be carefully
weighed against the costs (loss of autonomy through manipulation of decision)
throughout the whole process of model creation and application.
We start our ethical considerations at the collection and analysis of patient data by
means of an empirical study. Ethical codes, e.g., the ACM Code of Ethics or the ethical
guidelines of the GI, point out that the right for autonomy and privacy as well as the
protection of the human dignity has to be respected [34, 35, 36]. This includes the
sensibility of collected personal data. Therefore, a re-identification of anonymized
patient data has to be ruled out. As a possible technique, we suggest the introduction of
control groups for which no data is collected during the study. Additionally, the
collected data should be abstracted, e.g., by using probability distributions [37].
Nevertheless, patients may experience unpleasant consequences, such as realizing their
own weaknesses or exposing them to relatives during the experiment. Therefore, the
developer has to thoroughly weigh which data and causal relationships of variables are
really necessary for model generation and validation and can be published, with respect
to possible consequences for the individual care-dependent.
Second, the principles declare that the developer is responsible for negative
consequences resulting from using the model. This also includes injury of patients and
misuse of personal data. For example, the model could be used to test nudging methods
that advantage an interest group but disadvantage patients with negative consequences.
Thus, a careful consideration of possible effects for all concerned and the use of
approved scientific methods for model creation are recommended [34, 35]. Because
those nudging methods that prove to have the most influence on care-dependents’
behavior are meant to be applied to reality, the outcomes of the simulation have to be
reliable. Therefore, the evaluation of nudging methods should be based on defined
indicators that allow for evaluation as well as comparison of methods. In order to ensure
that care-dependents’ interests are followed the indicators should be defined
objectively. Additionally, an inadvertent misuse of the model can be prevented by a
detailed documentation of the model’s purpose, application or possible consequences.
4 Conclusion
In this paper, we discussed ethical implications, that might be faced during modeling
and simulation of nudging in care. A lot of attempts aim at phrasing rules in the field
of ATs as well as robotics and members of the EU Parliament made a corresponding
demand to the EU Commission in 2017 [38]. Although guidelines exist in the area of
modeling and simulation, there are only few limitations that developers have to face.
Additionally, ethical challenges that arise in modeling and simulation depend strongly
on the respective application area. Therefore, it is up to the developers to assume social
responsibility for their models and to respect formulated ethical principles.
Nevertheless, further discussion has to be started, to guarantee a thorough assessment
and to reinforce awareness of this issue.
38
References
1. Hackmann, T.: Arbeitsmarkt Pflege: Bestimmung der künftigen Altenpflegekräfte unter
Berücksichtigung der Berufsverweildauer. Sozialer Fortschritt 59, 235–244 (2010)
2. Statistisches Bundesamt: Pflege im Rahmen der Pflegeversicherung Deutschlandergebnisse
(Pflegestatistik) (2015)
3. Statistisches Bundesamt. Pflege im Rahmen der Pflegeversicherung Deutschlandergebnisse
(Pflegestatistik) (2017)
4. Rothgang, H., Müller, R., Unger, R.: Themenreport " Pflege 2030": was ist zu erwarten-was
ist zu tun?. Bertelsmann Stiftung (2012)
5. Thaler, R. H., Sunstein C. R.: Nudge: Improving decisions about health, wealth, and
happiness (2008)
6. Thomas, D. H., Jona, L.: 'Good Nudge Lullaby': Choice Architecture and Default Bias
Reinforcement. The Economic Journal, 128(610), 1180-1206 (2017).
7. Moglia, M., Podkalicka, A., McGregor, J.: An Agent-Based Model of Residential Energy
Efficiency Adoption. Journal of Artificial Societies & Social Simulation, 21(3) (2018)
8. Ölander, F., Thøgersen, J.: Informing versus nudging in environmental policy. Journal of
Consumer Policy. 37(3), 341-356 (2014)
9. Demarque, C., Charalambides, L., Hilton, D. J., Waroquier, L.: Nudging sustainable
consumption: The use of descriptive norms to promote a minority behavior in a realistic
online shopping environment. Journal of Environmental Psychology. 43, 166-174 (2015)
10. Ebert, P., Freibichler, W.: Nudge management: applying behavioural science to increase
knowledge worker productivity. Journal of Organization Design. 6(1), 4 (2017)
11. Stieglitz, S., Potthoff, T., Kißmer, T. Digital Nudging am Arbeitsplatz: Ein Ansatz zur
Steigerung der Technologieakzeptanz. HMD Praxis der Wirtschaftsinformatik. 54(6), 965–
976 (2017)
12. Galizzi, M. M.: Label, nudge or tax? A review of health policies for risky behaviours.
Journal of public health research. 1(1), 14 (2012)
13. Downs, J. S., Loewenstein, G., Wisdom, J.: Strategies for promoting healthier food choices.
American Economic Review. 99(2), 159-64 (2009)
14. Campbell-Arvai, V., Arvai, J., Kalof, L.: Motivating sustainable food choices: The role of
nudges, value orientation, and information provision. Environment and Behavior. 46(4),
453-475 (2014)
15. Orpwood, R., Adlam, T., Evans, N., Chadd, J., Self, D.: Evaluation of an assisted-living
smart home for someone with dementia. Journal of Assistive Technologies. 2(2), 13-21
(2008)
16. Namazi, K. H., & Johnson, B. D.: Physical environmental cues to reduce the problems of
incontinence in Alzheimer's disease units. American Journal of Alzheimer's Care and
Related Disorders & Research. 6(6), 22-28 (1991)
17. Mollenkopf, H., Schakib-Ekbatan, K., Oswald, F., Langer, N. für Alternsforschung, D. Z.:
Technische Unterstützung zur Erhaltung von Lebensqualität im Wohnbereich bei Demenz.
Ergebnisse einer Literatur-Recherche. Forschungsberichte aus dem DZFA. 16 (2005)
18. Hattori, H., Nakajima, Y., Ishida, T.: Learning from humans: Agent modeling with
individual human behaviors. IEEE Transactions on Systems, Man, and Cybernetics-Part A:
Systems and Humans, 41(1), 1-9 (2011)
19. Rodermund, S.C., Lorig, F., Berndt, J.O., Timm, I.J.: An Agent Architecture for Simulating
Communication Dynamics in Social Media. Multiagent System Technologies, 15th German
Conference (MATES), 19-37 (2017)
39
20. Bonabeau, E.: Agent-based modeling: Methods and techniques for simulating human
systems. Proceedings of the National Academy of Sciences. 99(suppl 3), 7280-7287 (2002)
21. Urban, C., Schmidt, B.: PECS–agent-based modelling of human behaviour. In Emotional
and Intelligent–The Tangled Knot of Social Cognition, AAAI Fall Symposium Series, North
Falmouth (2001)
22. Jager, W., Janssen, M.: The need for and development of behaviourally realistic agents. In
International Workshop on Multi-Agent Systems and Agent-Based Simulation. 36-49
(2002)
23. Berndt, J.O., Rodermund, S.C., Timm, I.J.: Social Contagion of Fertility: An Agent-Based
Simulation Study. Accepted for: 2018 Winter Simulation Conference (WSC 2018) (2018)
24. Reuter, L., Berndt, J.O., Timm, I.J.: Simulating Psychological Experiments: An Agent-Based
Modeling Approach. In: Folds, D.J.; Berndt, J.O. (Eds.): Fourth International Conference on
Human and Social Analytics (HUSO 2018). 5-10. IARIA: Wilmington, DE, USA (2018)
25. Gross, C. R., Lindquist, R. D., Woolley, A. C., Granieri, R., Allard, K., Webster, B.: Clinical
indicators of dehydration severity in elderly patients. The Journal of emergency medicine.
10(3), 267-274 (1992)
26. Fortmann, J., Cobus, V., Heuten, W., Boll, S.: WaterJewel: design and evaluation of a
bracelet to promote a better drinking behaviour. Proceedings of the 13th international
conference on mobile and ubiquitous multimedia, 58-67 ACM (2014)
27. Fortmann, J., Root, E., Boll, S., Heuten, W.: Tangible apps bracelet: Designing modular
wrist-worn digital jewellery for multiple purposes. In Proceedings of the 2016 ACM
Conference on Designing Interactive Systems, 841-852 ACM (2016)
28. Selinger, E., Whyte, K.: Is there a right way to nudge? The practice and ethics of choice
architecture. Sociology Compass. 5(10), 923-935 (2011)
29. Smith, N. C., Goldstein, D. G., Johnson, E. J.: Choice without awareness: Ethical and policy
implications of defaults. Journal of Public Policy & Marketing. 32(2), 159-172 (2013)
30. Beauchamp, Tom L.: The ‘four principles’ approach to health care ethics. Principles of
health care ethics. 29 3-10 (2007)
31. Kehl, C.: Robotik und assistive Neurotechnologien in der Pflege–gesellschaftliche
Herausforderungen (2018)
32. European Parliament: Charter of fundamental rights of the European Union. Office for
Official Publications of the European Communities (2000)
33. Deklaration von Helsinki: Ethische Grundsätze für die medizinische Forschung von
Menschen.
http://www.bundesaerztekammer.de/fileadmin/user_upload/Deklaration_von_Helsinki_20
13_DE.pdf (2013)
34. ACM 2018 Task Force: ACM Code of Ethics and Professional Conduct.
https://www.acm.org/code-of-ethics (2018) (Accessed: 28. 11. 2018)
35. Gesellschaft für Informatik: Die ethischen Leitlinien der Gesellschaft für Informatik e.V.
https://gi.de/fileadmin/GI/Allgemein/PDF/GI_Ethische_Leitlinien_2018.pdf (2018)
36. Oren, T. I.: Ethics in modeling and simulation (simethics). Interservice/Industry Training,
Simulation, and Education Conference (I/ITSEC) (2005)
37. Bier, C., Krempel, E.: Common privacy patterns in video surveillance and smart energy.
ICCCT-2012, 610-615 (2012)
38. Europäisches Parlament: Zivilrechtliche Regelungen im Bereich Robotik – Entschließung
des Europäischen Parlaments (P8_TA(2017)00 51) vom 16. Februar 2017 mit
Empfehlungen an die Kommission zu zivilrechtlichen Regelungen im Bereich Robotik
(2015/2103(INL)). http://www.europarl.europa.eu/sides/getDoc.do?pubRef=-
40
//EP//NONSGML+TA+P8-TA-2017-0051+0+DOC+PDF+V0//DE (2017) (Accessed: 03.
12. 2018)
41