Speakers & Abstracts

Gwen Marchand, Associate Professor, Department of Educational Psychology and Higher Education, University of Nevada, Las Vegas

Gwen C. Marchand, PhD, is the Associate Dean for Research and Sponsored Projects in the College of Education and an Associate Professor in the Department of Educational Psychology and Higher Education at the University of Nevada-Las Vegas (UNLV). Her research interests include academic motivation and engagement classroom systems, student mobility, collaborative team processes, and research design and methods from a complex systems perspective. As the former Director of the UNLV Center for Research, Evaluation, and Assessment, she led evaluations of interdisciplinary research centers and institutes using innovative methods. Dr. Marchand is currently the principal investigator on a collaborative National Science Foundation project.  Her work has been published in top journals including Educational Psychologist, Contemporary Educational Psychology, and the Journal of Educational Psychology.

Complexity Science in the Design and Evaluation of Behaviour Interventions

Complex systems perspectives require a fundamental shift in the way scholars and decision makers conceptualize phenomena they are trying to change. Approaching behavior change from a complexity perspective requires consideration of phenomena themselves as complex systems, defined as collections of interacting elements that give rise to emergent behavior. Hallmarks of complex systems approaches to research in the social sciences include a focus on systems architecture, or the different levels of the individual and social systems, and systems behavior, or the stability and change in human behavior and transactions amongst levels. This talk focuses on the use of complex systems conceptualizations and methods to identify points for intervention, assist in intervention design, and monitor intervention processes and outcomes. Drawing upon examples from education and social sciences, the presenter will discuss research design considerations from a complex systems perspective, with a focus on dynamics and change and multilevel interactions. Key questions that practitioners and researchers should query include: What is the appropriate level of the system and timescale to measure and monitor the behavioral patterns associated with an intervention? Why might individuals respond differently to attempts at behavior change and are there different pathways to similar desired states? How does the context for the intervention constrain the behavior possibilities for the system?

Main learning points:

What does it mean to define behavior and behavior change from a complex systems perspective?

Focal units and well-defined timescales are key considerations for design and research of intervention 

Context acts to constrain and afford possible states for behavior change related to intervention

Ken Resnicow, Irwin M. Rosenstock Collegiate Professor, University of Michigan School of Public Health Department of Health Behavior & Health Education

Ken Resnicow is the Irwin Rosenstock Professor of Health Behavior and Health Education at University of Michigan School of Public Health and Professor of Pediatrics in the School of Medicine, the Associate Director for Community Engagement and Health Disparities Research at the University of Michigan Rogel Cancer Center, and Chief Scientific Lead at the University’s Center for Health Communications Research. His work over the past 30 years has focused on designing and evaluating behavior change programs for a wide range of health behaviors including smoking cessation, breast cancer treatment, genetic testing, cancer screening, car safety, weight control, diet and physical activity, effective parenting, medical adherence, organ donation, substance use, youth violence and risk behaviors, and accrual into clinical trials.   He has published over 340 peer-reviewed articles and book chapters and has served on numerous advisory panels and review groups. He has been PI or Co-Investigator on over 80 NIH and other externally funded grants. In 2019 he joined the National Council for the National Institute on Minority Health and Health Disparities (NIH).

 In recent years, his work has increasingly entailed novel behavioral tailoring and the incorporation of e-Health technology to enhance the impact of health messages.  He has collaborated with researchers in over 25 countries including; South Africa, Australia, Mexico, Brazil, Portugal, Romania, the Ukraine, and the Netherlands and has trained over 1000 health professionals in Motivational Interviewing in both academic and health care delivery settings.

Behavior Change is a Complex Process.  How does that impact theory, research and practice?

The abstract to be announced later.

Main learning points:

Behavior change is a complex, non linear process

Intervention effects are complex; moderation is a form of complexity

Sudden change is more enduring than gradual change

Failure to replicate prior interventions can be understood from a complexity lens

Jari Saramäki, Professor of Computational science, Aalto University

Professor Jari Saramäki is a network scientist, working at Aalto University, Finland. He received his PhD in applied physics in 1998, studying quantum crystals at milliKelvin temperatures. After some career twists and turns involving technology companies and what we would nowadays call data science, he returned to academia to study complex networks, a new and rapidly expanding field at that time. Jari Saramäki is probably best known for his work on social and temporal networks, but his broad range of research interests has included topics from ant supercolonies to the human immune system.

How do behaviours, ideas, and contagious diseases spread through networks?

I will present an overview on how the networks of interactions between people influence the spreading of ideas, information, behaviours, or contagious disease. My focus is on key features of social network structure and how they influence spreading processes, from the existence of hub nodes to correlations between tie strengths and network structure and to temporal features such as burstiness of interactions. I will also discuss “network interventions” — ways of modifying the network structure for the purpose of either promoting or hindering spread. 

Main learning points:

People are embedded in networks that influence their behaviour and health,

Network structure — how the networks’ links are organized — strongly affects this influence,

Interventions that modify network structure can be used to promote or hinder the spread of influence or contagion.

Olli-Pekka Heinonen, Director General, Finnish National Agency for Education

Olli-Pekka Heinonen is the Director General at the Finnish National Agency of Education. Mr Heinonen has been the Minister of Education and Science (1994-1999), Minister of Transport and Communication (1999-2002) and MP (1995-2002). He has also been the Television Director at the Finnish Public Broadcasting Company and State Secretary in five different ministries. Mr Heinonen has held various positions of trust within different sectors of society. Mr Heinonen holds a Master’s Degree in Law. He is married and has three children.

Complexity-informed policymaking

The structures, organisations, management cultures and change implementation practices were created in a world which was seen as being more predictable and linear by nature. As the reality now unveils itself to us as being more complex and interconnective, the path dependency of old thinking and implementing carries on. The presenter describes based on his own experience how the complexity-informed decision-making is necessary in turning the decisions to intended changes. The way of implementation becomes crucial, and emergent learning processes are valuable in dealing with wicked problems.

Main learning points: 

What does it mean to define behavior and behavior change from a complex systems perspective?

Focal units and well-defined timescales are key considerations for design and research of intervention.

Context acts to constrain and afford possible states for behavior change related to intervention.



Marijn de Bruin, Professor, Radboud University Medical Center

Integrating behavioural science in COVID-19 prevention efforts: the Dutch case

Matti Heino, Doctoral Student of Social Psychology, University of Helsinki

Matti is a social psychology doctoral student, who used to used to work in behaviour change topics in the sales and marketing world, prior to his involvement in health behaviour intervention research. Most of his work deals with improving physical activity and work motivation, as well as methodology of health psychology research. He is also a Real World Risk Institute alumnus and an avid self-quantifier, having collected some 50 000 experience sampling data points over the past decade.

Studying complex motivation systems: Capturing dynamical patterns of change in data from self-assessments and wearable technology

The analytical methods currently used in the behavioural sciences rely on simplifying assumptions leading back several centuries. During recent decades, though, major advances have been made in the study of complex systems, and new tools are now available to behavioural scientists, too. This talk exhibits three commonly encountered features in psychological processes – interconnectedness, non-ergodicity and non-linearity – which originate from principles common to all living systems. To study change in systems with these features, researchers have applied methods of multiplex recurrence network analysis on densely sampled data collected by smartphones. This allows the investigator to infer driving forces in the person’s motivational system. These drivers are likely to be meaningfully different from one individual to the next; to avoid fitting an individual into a Procrustean bed of theory, we need to analyse the person’s idiographic data before aggregating it in a search of commonalities among individuals. Opportunities have also arisen for assessing critical fluctuations as early warning signals before change occurs. After more than two decades in incubation, practical complexity science is now entering behaviour change research with exciting opportunities for future research.

Main learning points:

Analysis of living beings involves addressing interconnected, turbulent processes that vary across time. Recruiting less individuals and collecting more data on fewer variables, may be a considerably beneficial tradeoff to better understand dynamics of a psychological phenomenon. From such data, drivers central to a person’s motivational system can be identified, leading to interesting applications.

Daniele Proverbio, Doctoral researcher, Luxembourg Centre for Systems Biomedicine, University of Luxembourg

Daniele Proverbio is a doctoral student in the Systems Control Group at the Luxembourg Center for Systems Biomedicine (University of Luxemburg) and visiting doctoral fellow at University of Exeter. He had graduated in Physics of Complex Systems and is currently applying quantitative modelling to multidisciplinary complex subjects. Among his extra projects, he curates an Instagram channel on scientific information for the broad public.

Smooth or abrupt? How dynamical systems change their state

Rather than evolving smoothly and linearly, real world phenomena often exhibit rapid, abrupt changes between alternate states. These transitions have been extensively observed e.g. in ecology, physics, biology, climate science and so on. As increasing evidence is also being collected in the field of Health Behaviour, it is compelling to characterize and understand such “critical” transitions and their implications.

This presentation introduces a Complex Systems modelling perspective, thus bridging Dynamical System theory and phenomenological system properties. Moreover, it presents a classification of the abrupt transitions that are currently known, inquiring their main drivers and identifying the universal dynamical patterns that are shared among different systems. Finally, I will discuss preliminary measures to detect and predict abrupt transitions in real systems, and further applications.

Main learning points:

Natural phenomena don’t necessarily follow smooth and linear patterns while evolving.

Abrupt changes are common in complex, non-linear systems. These are arguably the future of scientific research.

There exist a limited number of transition classes. Understanding their main drivers could lead to useful insights and applications.

Ira Alanko, AuroraAI Program Manager, Ministry of Finance, Finland

Ira Alanko, MScEcon, is the Programme Manager of the Finnish National Artificial Intelligence Programme AuroraAI. AuroraAI will make use of artificial intelligence (AI) to create models that can drive forward a systemic change towards a human-centric society. She has a track-record of promoting innovation and experimentation plus development of eServices in Finland from a ministerial level.

The AuroraAI Programme

The AuroraAI Programme (2020–2022) is based on the strategic objective of building a dynamic and thriving Finland, as expressed in the Programme of Prime Minister Sanna Marin’s Government. The aim is to make everyday life easier with the aid of the AuroraAI network, in a secure and ethically sustainable way. To achieve this, artificial intelligence (AI) will be utilized to create models that can drive forward the best public administration in the world.

Public sector organisations will be linked together using the AuroraAI network, enabling AI to facilitate their interaction with the services provided by other sectors. Breaking down silos that continue to affect parts of the current service provision, the AuroraAI network will help determine which individuals are in need of a particular service. This will improve the match between users and public services while tackling inefficiency and resource waste. The purpose of AuroraAI is to create the technical conditions that enable information exchange and interoperability between different services and platforms. Seamless interaction between them will require, among other things, joint development of interfaces and communication between the development teams.

Main learning points:

The Finnish public sector is taking active steps to utilise AI to make using of  services easier

AI has opened a window for a systemic shift towards human-centricity in Finland

The AuroraAI-network is a collection of different components, not a platform or collection of chatbots

Nanne Isokuortti, Doctoral researcher in Social Work, the Faculty of Social Sciences, University of Helsinki

Nanne Isokuortti is a doctoral researcher in Social Work at the Faculty of Social Sciences at the University of Helsinki, Finland. Her research interests include implementation of evidence-informed practices in children’s social services. In recent years, she has visited University of Melbourne, School of Health Sciences and University of Oxford, Department of Social Policy and Intervention. Prior joining the faculty, she has worked in social work practice and social and health care administration.

From exploration to sustainment: understanding complex implementation in public social services

This presentation explores complexities in implementation in public social services with a real-world case example. The case focuses on nation-wide, government-initiated implementation of the Systemic Practice Model (SPM) in Finland. Originally developed in the United Kingdom, the model aims to improve children’s social services by integrating systemic family therapy with statutory social work. In the initial phase, the SPM was implemented 31 municipal children’s service sites located in 14 counties around the country. The Exploration, Preparation, Implementation, and Sustainment (EPIS) Framework is used in illustrating the complexities, such as involvement of multiple participants from different levels and the complexity of model itself, embedded in the process. The EPIS framework is a multi-level, four-phase model designed to guide and describe the implementation process in public service sectors. The presentation concludes with practical implications how to aid implementation in complex service systems.

Main learning points:

The presentation aims to:

Illustrate the complexity in an implementation process with a real-world case example

Introduce Exploration, Preparation, Implementation, and Sustainment (EPIS) Framework

Provide suggestions how to aid implementation in complex settings