At-tractor what-tractor? Tipping points in behaviour change science, with applications to risk management

Back in 2020, our research group was delivering the last of five symposia included in a project called Behaviour Change Science and Policy (BeSP). I was particularly excited about this one because the topic was complexity, and the symposia series brought together researchers and policy makers interested in improving the society – without making things worse by assuming an overly narrow view of the world.

I had a particular interest in two speakers. Ken Resnicow had done inspiring conceptual work on the topic already back in 2006, and had been an influence on both me and my PhD supervisor (and BeSP project lead) Nelli Hankonen, in her early career. Sadly, the world hadn’t yet been ready for an extensive uptake of the ideas, and much of the methodological tools were inaccessible (or unsuitable) to social scientists. The other person of particular interest, Daniele Proverbio, on the other hand, was a doctoral researcher with training in physics and systems biology; I had met him by chance at the International Conference on Complex Systems, which I probably wouldn’t have attended, had it not been held online due to COVID. He was working on robust foundations and real-world applications of systems with so-called tipping points.

I started writing a paper with Ken, Daniele, Nelli and Gwen Marchand, who was also speaking at the symposium, as she had been working extensively on complexity in education. The paper started out as an introduction to complexity for behaviour change researchers, but as I took up a position in the newly founded behavioural science advisory group at the Finnish Prime Minister’s Office late 2020, the whole thing went to a back burner. It wasn’t just that, though. Being a scholar of motivation, I knew that being bored of your own words is a major warning sign, and things you prefer not to eat, you shouldn’t feed to others. So I didn’t touch the draft for over a year.

Meanwhile, I finished a manuscript which started out as a collection of notes from arguments about study design and analysis within our research group, when we were doing a workplace motivation self-management / self-organisation intervention. The manuscript demonstrated, how non-linearity, non-ergodicity and interdependence can be fatal for traditional methods of analysis. It was promptly rejected from Health Psychology Review, the flagship journal of the European Health Psychology Society – on the grounds that linear methods can solve all the issues, which was exactly the opposite of manuscript’s argument. That piece was later published in Behavioural Sciences, outlining the foundations of complex systems approaches in behaviour change science.

As the complexity fundamentals paper had now been written, I wasn’t too keen on continuing on our BeSP piece, before I was hit by a strange moment where everything I had dabbled with (and discussed with Daniele) for the previous year sort of came together. I re-wrote the entire paper in a very short time, partly around analysis I had started due to natural curiosity with no particular goal in mind.

This is non-linearity in action: instead of “productively” writing a little every day, you write nothing for a very long time, and then everything at once. And this is not a pathology – except in the minds of people who think everything in life should follow a pre-planned process of gradual fulfillment. I’ve spent decades trying to unlearn this, so I should know.

The paper turned out very non-boring to me, and I was particularly happy the aforementioned flagship journal (the one which rejected the earlier piece) accepted it with no requests for edits – despite being based on the same underlying ideas as the earlier one.

Graphical abstract of the attractor landscapes paper; courtesy of Daniele Proverbio. Describes two types of tipping points in systems with attractors.
Graphical abstract of the attractor landscapes paper; courtesy of Daniele Proverbio.

Implications to risk management

The theory underlying attractor landscapes and tipping points, points to two important issues in risk management. Firstly, large changes need not be the result of large events, but small pushes can suffice, when the system resides in a shallow attractor or on the top of a “hill” in the landscape. Secondly, the fact that earlier events have not caused large-scale behaviour change, does not imply that they continue not do so in the future. This is a mistake constantly made by Finnish doctors and epidemiologists throughout the pandemic, e.g. about people’s unwillingness to take up masks – we could stop COVID, for example, but don’t do so because people have been told this attractor is inescapable.

In a recent training for public servants, we experimented with conveying these ideas to non-scientists – lots of work to be done, but some did find it an enlightening escape from conventional linear thinking.

To sum up, some personal takeaways (your mileage may vary):

  1. The quality of motivation you experience when working on something boring is information: there might be a better idea, one actually worth your time, which gets trampled as you muddle through something less attractive. Same applies to health behaviours.
  2. Remain able to seize opportunities when they arise: steer clear of projects with deadlines, and milestones in particular. They coerce you to finish what you started, instead of dropping it for a time and starting anew much later.

The astute reader may have noticed, that I did not explain the damned attractors in this post at all. You’ll find all you need here:

What Does “Behaviour Change Science” Study?

This is an introductory post about this paper. The paper introduces to the object of study in “behaviour change science”, i.e. complex systems – which include most human systems from individuals to communities and nations.

In a health psychology conference many years ago (when we still travelled for that sort of thing), I wandered into the conference venue a bit late, and the sessions had already started. There was just one other person in the hallways, looking a bit lost. I was scared to death of another difficult-to-escape presentation cavalcade about how someone came up with p-values under 0.05, so I made some joke about our confusion and ended up preventing his attendance, too. Turned out he was a physicist recently hired in a behavioural medicine research group, sent to the conference to get his first bearings about the field. Understandably, he was confused with a hint of distraught: “I don’t understand a word about what these people talk about. And I’ve been to several sessions already without having seen a single equation!” (nb. if you don’t think this is funny, you’re probably not a social scientist.)

Given that back then I was finding my first bearings on network science, we had a lot to talk about during the rest of the conference. I don’t remember much about the conference, but I remember him making an excellent point about learning: The best way to learn anything is to talk to someone who’s just learned about the thing. While not yet mega-experts, they still have an idea of where you stand, and can hence make things much more understandable than those, who already swim in a sea of concepts unfamiliar to you.

In a recent paper about behaviour change as a topic of research, we tried to do exactly this. I know I’m crossing the chasm where I’m not yet the mega-expert, but am already losing the ability to see what people in my field find hard to grasp. I presented the paper in a research seminar and people found it quite challenging, but on the other hand, I’ve never seen such ultra-positivity from reviewers. So maybe it’s helpful to some.

This impeccably written manuscript provides a thorough, state-of-the-art review of complex adaptive systems, particularly in the context of behavior change, and it does an excellent job explaining difficult concepts.

– Reviewer 2

Here’s a quick test to see if it might be valuable to you. Have a look at this table, and if you think all is clear, you can skip the piece with good conscience:

I also made a video introduction to the topic. If you’re in a rush, you can just run through a pdf of the slides.

If you’re in an even bigger rush, the picture below gives a quick synopsis. To find out more, check out this post:

The Complexity Matters Vodcast

On Fred Hasselman‘s initiative, we started a new show where we host a live-streamed discussion on complexity topics. I will gather a list of episodes with synopses in this post.

Note: The next episode is scheduled to take place on 12 January at 12:30 CET, when we interrogate Travis Wiltshire on issues regarding team dynamics!

S01E01: Complexity in psychological self-ratings.

We discussed Merlijn Olthof’s new paper Complexity in psychological self-ratings: implications for research and practice. Links are found in video comments on the YouTube page, but here are some extras:

Additional resources:

Interaction is not interaction: An interview with Fred Hasselman

I had the opportunity to interview Fred Hasselman, the main architect of casnet: An R toolbox for studying Complex Adaptive Systems and NETworks. We spoke of how compatible the complex systems perspective is with some methods widely used in social sciences.

A few notes:

  • Multilevel models (and what you put in those) come in many varietiesand some are useful
  • Interaction is not interaction
    • Interaction (1): Two variables are intertwined – or “coupled” – in such a way, that they cannot be separated without severing the phenomena arising from their interplay.
    • Interaction (2): A multiplicative, instead of additive, relationship in a linear regression model, where you can partial out variance and get nice beta weights for each variable to determine their individual impacts.
    • The two meanings presented above are logically inconsistent: See #36 in Scott Lilienfield’s “Fifty psychological and psychiatric terms to avoid
  • Interdependence means you can’t use the regular statistics which social scientists know and love.
    • … because you lose additivity.
  • “Don’t infer causality, observe it.”
    • When the system you’re looking at is an individual instead of e.g. the society, you’re in the quite happy position, that lab studies are possible (if you’re smart about them).
  • An excellent paper from Merljin Olthof: Complexity in psychological self-ratings: implications for research and practice
  • Additional resources:
    • A symposium we held on complexity in behavioural science, evidence and policy.
    • A workshop by Fred Hasselman (scroll to the end for an extensive reading list).
    • University of Helsinki course by Matti: CARMA – Critical Appraisal of Research Methods and Analysis.

Because every post needs an image, here’s Julia Rohrer‘s (2017) Theory of Regulation of Empty Theories (TROETE)

Complexity perspectives on behaviour change interventions

I had the great pleasure to be involved in organising a symposium on the topic of my dissertation. Many if not most societal problems are both behavioural and complex; hence the speakers’ backgrounds varied from systems science, and psychology to social work and physics. Below is a list of video links along with a short synopsis of the talks. See here for other symposia in the Behaviour Change Science and Policy series.

A live-tweeting thread on 1st day here, 2nd day (not including presentations by me, Nanne Isokuortti or Ira Alanko) here. See here for the official web page, and here for the YouTube playlist!

Nelli Hankonen: Opening words & introduction to the Behaviour Change Science & Policy (BeSP) project

  • See here for videos of previous symposia (I: Intervention evaluation & field experiments; II: Behavioural insights in developing public policy and interventions; III: Reverse translation: Practice-based evidence; IV: Creating real-world impact: Implementation and dissemination of behaviour change interventions)

Marijn de Bruijn: Integrating Behavioural Science in COVID-19 Prevention Efforts – The Dutch Case

  • Behaviour change efforts for COVID-19 protective behaviours are operations on a complex system’s user experience: A virus is the problem, but behaviour is the solution.
  • Knee-jerk communication responses of health officials can be improved upon by using methods derived from what works in real-world behavioural science interventions.
  • Protective behaviours entail feedback dynamics: for example, crowding leads to difficulty maintaining distance, which leads to perceiving that others don’t consider it important, which leads to more crowding, etc.

Nelli Hankonen: Why is it Useful to Consider Complexity Insights in Behaviour Change Research?

  • Complexity-informed approaches to intervention have been around for a long time, but only recently analytical methodology has become widely available.
  • There are important differences between “complicated” and “complex” behavioural interventions.
  • By not taking the complexity perspective into account, we may be missing opportunities to properly design interventions.

Olli-Pekka Heinonen: Complexity-Informed Policymaking

  • If a civil servant wants to be effective, maximum control doesn’t work – even what constitutes “progress” can be difficult to ascertain.
  • Systems, such as the society, move: what worked yesterday, might not work today.
  • Hence continuous learning, adaptation and experimenting are not optional for societal decision-making.

Gwen Marchand: Complexity Science in the Design and Evaluation of Behaviour Interventions

  • 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

Jari Saramäki: How do Behaviours, Ideas, and Contagious Diseases Spread Through Networks?

  • 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.

Matti Heino: Studying Complex Motivation Systems – Capturing Dynamical Patterns of Change in Data from Self-assessments and Wearable Technology

  • 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.
  • Methods to deal with such data include building networks of networks (multiplex recurrence networks) and assessing early warning signals of sudden gains or losses.

If you’re interested in the links, download my slides here. I actually forgot to show what a multiplex network of variables combined from several theories looks like (you don’t condition on all other variables, so you can combine stuff from different frameworks without the meaning of the variables changing, as in a regression-based analysis). Anyway, it looks like this:

A single person’s multiplex recurrence network, i.e. a network of recurrence networks of work motivation variables queried daily for 30+ days. Colored connectors are relationships which can’t be attributed to randomness.

Nanne Isokuortti: From Exploration to Sustainment – Understanding Complex Implementation in Public Social Services

  • 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

Ira Alanko: The AuroraAI Programme

  • 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

Daniele Proverbio: Smooth or Abrupt? How Dynamical Systems Change Their State

  • 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.

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

  • Behavior change is a complex, non linear process.
  • Sudden change is more enduring than gradual change.
  • Failure to replicate prior interventions can be understood from a complexity lens.

(nb. on the last talk: personally, I’m not a huge fan of mediation analysis, moderated or otherwise. Stay tuned for an interview where I discuss the topic at some length with Fred Hasselman)

Notes from the symposium by Grace Lau

Suomen tie ulos kriisistä

Yaneer Bar-Yam is a complexity scientist, who has worked with and warned about pandemics for 15 years. His interview with Esa-Pekka Pälvimäki and Thomas Brand (in English) regarding the COVID-19 situation in Finland can be found here; these are some of my notes and video extracts.

Nämä ovat muistiinpanoni Esa-Pekka Pälvimäen ja Thomas Brandin toimittamasta kompleksisuustieteilijä ja pandemiatutkija Yaneer Bar-Yamin haastattelusta, jossa tämä kommentoi Suomen Koronavirustilannetta. Bar-Yam on yksi kompleksisuustieteiden isistä, ja äärimmäisen kunnioitettu tutkija. Ks. myös Suomenkielisiä työkaluja COVID-19 taisteluun.

Ensisijainen asia: On ymmärrettävä, että voimme päästä taudista eroon. Voimme lopettaa tämän taudin siinä, missä olemme lopettaneet muitakin tauteja: SARS, MERS ja Ebola eivät ole globaaleja riesoja. Tästä lisää myöhemmin.

“On maita, jotka ovat toimineet viisaasti ja päässeet taudista eroon; ne ovat [historian silmissä] voittajia. Suomi ei ole vielä siellä… jos Suomi haluaa päästä johtajien joukkoon, sen tulee toimia nopeasti ja voimakkaasti taudin hävittämiseksi.”

Kaksi tietä ulos kriisistä:

  1. Kahden viikon sulku johtaa siihen, että uudet tautitapaukset loppuvat lähes kokonaan. Niillä alueilla, joilla edelleen on tapauksia, sulkua tulee jatkaa. Hallituksen tulisi tukea kaupunkien ja muiden yhteisöjen päätösvaltaa siinä, että nämä voivat säädellä itse omia rajoituksiaan.
  2. Viiden viikon kansallinen sulku: On paljon maita, jotka ovat menestyneet COVID-taistossa kansallisen sulun avulla (esim. Etelä-Korea, Kreikka, Islanti, Luxemburg, Kroatia; ks. kuva). Tähän joukkoon kuuluvat maat voivat avata matkustusrajoituksia toistensa välillä. Toim. huom. yksikään maa ei ole peitonnut virusta ilman päättäväisiä vastatoimia.

Mikä on yhteisöjen rooli epidemian torjunnassa? Mikäli lainvoimainen ulkonaliikkumiskielto tai muut liikkumisrajoitteet ovat mahdottomia, pandemiavaste voidaan tehdä yhteisöissä; viestinä on, että olemme samassa veneessä ja kaikki haluavat päästä takaisin normaaliin – palataan siis normaaliin mahdollisimman nopeasti! Kaikki eivät suosituksia tietenkään tule noudattamaan, mutta jos suurin osa niin tekee, se riittää. Yleinen ja hyväksi havaittu tekniikka epidemian hallintaan on ovelta ovelle kulkeminen ja yhteisön jäsenten voinnin tiedusteleminen; ovatko he terveitä, sairaita, tarvitsevatko he jotakin? Tätä voi pari viikkoa tehdä yhteisön jäsen, Suomessa kenties taloyhtiön suojelu/turvallisuusvastaava?

Yhteisöissä, joissa tauti leviää poikkeuksellisen vahvasti, tulee puhua johtajille ja kertoa, että taudista ja sen tuottamasta kärsimyksestä voidaan päästä eroon. Ei ole mitään tärkeämpää kuin se, että yhteisöt saadaan ottamaan omistajuus ja vastuu omista jäsenistään. Heitä, heidän huoliaan ja ongelmiaan tulee kuunnella ja kysyä, kuinka heitä voitaisiin parhaalla tavalla auttaa.


Käynnistyykö leviäminen väistämättä uudestaan, jos tautia kantava henkilö pääsee tartunnoista vapaalle alueelle? Uudet tartunta-aallot eivät ole tarpeellisia. Tartuntatauteja on hävitetty ennenkin, ja samoin voidaan Koronaviruskin hävittää: paikallisesti ja globaalisti. Kyse on valinnasta. Esim. 1-3 tapausta voidaan aina pysäyttää kontaktijäljityksen ja altistuneiden eristämisen avulla; voimme myös toimillamme vaikuttaa siihen, että tapausten ilmaantuminen on hyvin epätodennäköistä. Mutta jos tapauksia on esim. kymmenen, tarvitaan järeämpiä toimia.

Palaako tauti aina ja ikuisesti ulkomailta, kunnes rokotus on saatavilla; eihän minkään maan talous kestä niin pitkiä rajoituksia? Ei, taudin hävittäminen saadaan tehtyä viikoissa. Suomessa se saataisiin poistettua monista paikoista kahdessa viikossa, toisissa kolmessa tai useammassa. Viidessä-kuudessa viikossa se katoaisi kaikkialta. Tähän liittyy kiinnostava harha: taudin alkuvaiheessa ajateltiin taudittoman maailman kestävän ikuisesti, ja nyt ajatellaan taudin kestävän ikuisesti. Ei – normaalitila ei kestä ikuisesti, eikä poikkeustila kestä ikuisesti. SARS ja MERS eivät päätyneet nekään kiertämään maailmaa ikuisesti.


Entä laumaimmuniteetti? Laumaimmuniteetin hankinnan kustannus on valtava, eikä ole selvää, että se toimisi. Jos emme tee suurempia toimia, yritämme pitää tautitapaukset alhaalla ja odotamme rokotetta sekä laumaimmuniteettia, siinä voi mennä vuosia, ja se voi maksaa 250 000 henkeä.

Keskustelussa sivuttiin seitsemää ensimmäistä kohtaa 9-kohtaisesta toimintaohjelmasta (ks. COVID-19: How to Win, sekä suomennetut ohjeet: COVID-19 -taistelusuositukset poliittisille päätöksentekijöille | Miksi viiden viikon lockdown voi pysäyttää COVID19-epidemian? | Milloin voimme jälleen avata yhteiskunnan?)

  1. Kaikkien – yritysten, yhteisöjen ja hallituksen – saaminen mukaan ponnistukseen.
  2. Sulku (lockdown): Fyysisen etäisyyden (6–9 metriä; 2m ei riitä) pitäminen, tartuntojen rajoittaminen perheryhmissä (positiiviseksi testatut henkilöt lähetetään karanteeniin esim. hotelliin oman asunnon sijaan).
  3. Tapausten tunnistaminen ja eristäminen (miellyttäviin paikkoihin, esim. hotelleihin) ajoissa.
  4. Kasvosuojainten käyttäminen, erityisesti välttämättömissä palveluissa.
  5. Edes jonkinasteiset matkustusrajoitukset. Liikkumisen rajoittaminen tarpeellisiksi nähtyihin palveluihin on parempi kuin se, että kaikki liikkuvat mielin määrin, mikä tuo tartuntatapaukset paikkoihin joissa niitä ei välttämättä muuten olisi.
  6. Välttämättömien palveluiden käytön saaminen turvalliseksi. Turvalliset työtilat, etätyömahdollisuudet, ruokakauppojen kotiin-/kadullekuljetukset, jne.
  7. Laajamittainen testaus, jotta tiedetään missä tarvitaan lisärajoituksia ja missä rajoituksia voidaan höllentää. Tietokonetomografiaa voidaan käyttää testaamisen nostamiseen uudelle tasolle; se tuottaa hyvin vähän vääriä negatiivisia havaintoja.


“Kuka tahansa, joka nykyään sanoo, ettei ole olemassa informaatiota, jonka perusteella kasvosuojainten voi sanoa olevan hyödyllisiä taudin leviämisen kannalta, on sokea. He pitävät maskia suun ja nenän sijaan silmillään. Näyttö on olemassa, tieteellinen ymmärrys on olemassa; tämän viestin pitää olla selkeä.”

– Yaneer Bar-Yam (39:34)

Valtava virhe vastatoimissa on, että ajattelemme ja toimimme kuin tämä olisi influenssa. Mutta siitä ei ole kyse; voimme oppia enemmän vakavista tartuntatautitapauksista selvinneiltä mailta, kuin voimme menneestä toiminnastamme vanhojen perus-influenssojen parissa. Esimerkiksi Ebola on tullut paikallisen häviämisen jälkeen takaisin vain siksi, että se on palannut eläinten kautta ihmisiin.


Vaihtoehtoinen strategia ei ole Flatten the Curve, ts. “Pidä tapausmäärät alhaalla ja odottele”.


Tässä vielä video kokonaisuudessaan:


Complexity methods for behavioural sciences: YouTube channel and resources

In 2019 I attended an exciting summer school; Complexity Methods for Behavioural Science: A Toolbox for Studying Change. Later, we – that is, the University of Helsinki Behaviour Change and Wellbeing Group – had the opportunity to invite Fred Hasselman, who devised the course, to Finland. He gave an overview talk as well as a 3-day workshop, which I recorded with varying success. This page collates resources regarding the course.

For all the recordings, see our YouTube channel. There are two playlists; one for short snippets and another one for full-length lectures. Here are some tweets on the course, with links to further resources. For additional slides, see here. See the end of the post for literature!

  • Lecture 0 (video, slides)Overview of complexity science and its applications in behavioural sciences. Also see shorter snippets on ergodicity, interaction- vs. component-dominant dynamics, and my interview with Fred.
  • Lecture 1 (video, slides [1-25])Introduction to Complexity Science: Dissipative systems, Self-Organization, Self-Organised Criticality (SOC), Phase transition, Interaction Dominant Dynamics, Emergence, Synchronisation.
  • Lecture 2 (video, slides [26-74])Introduction to the mathematics of change: Logistic Map, Return Plot, Attractors. [The beginning of the lecture was cut due to camera problems; please find a great introduction to the logistic map here.]
  • Lecture 3 (video, slides)Basic Time Series Analysis: Autocorrelation Function, Sample Entropy, Relative Roughness.
  • Lecture 4 (video, slides [34 onwards, see also this, this and this]) – Detecting (nonlinear) structure in time series: Fractal Dimension, Detrended Fluctuation Analysis, Standardised Dispersion Analysis.
  • Lecture 5 (video, slides [1-16])Quantifying temporal patterns in unordered categorical time series data: Categorical Auto-Recurrence Quantification Analysis (RQA).
  • Lecture 6 (video, slides [17-52])Quantifying temporal patterns in continuous time series data: Continuous Auto-Recurrence Quantification Analysis, Phase-space reconstruction.
  • Lecture 7 (video, slides [52-70]) – Recurrence Quantification Analysis in practice: Data preparation for RQA, “General recipe” (i.e. RQA workflow), lagged/windowed RQA, RQA in detecting cognitive phase transitions, RQA in neural imaging.
  • Lecture 8 (video, slides) – Multivariate Recurrence Quantification Analysis: Cross-Recurrence Quantification Analysis (CRQA), applications in interpersonal synchronisation dynamics (leader-follower behaviour), Diagonal Cross-Recurrence Profiles (DCRP).
  • Lecture 9 (video, slides) – Multivariate Time Series Analysis – Dynamic Complexity & Phase Transitions in Psychology: Self-ratings as a research tool, the importance of sampling frequency, dynamic complexity as an early warning signal in psychopathology.
  • Lecture 10 (video, slides [1-37]) – Introduction to graph theory, with applications of network science: Complex networks, hyperset theory, network-based complexity measures, small-world networks.
  • Lecture 11 (video, slides [38-80]) – Multiplex recurrence networks for non-linear multivariate time series analysis: Recurrence networks, change profiles of ecological momentary assessments as an alternative to raw scores. Also see this paper!

Matti spiral


Three recent papers directly related to the course’s topics:

Hasselman, F., & Bosman, A. M. T. (2020). Studying Complex Adaptive Systems with Internal States: A Recurrence Network Approach to the Analysis of Multivariate Time Series Data Representing Self-Reports of Human Experience. Frontiers in Applied Mathematics and Statistics, 6.

Heino, M. T. J., Knittle, K. P., Noone, C., Hasselman, F., & Hankonen, N. (2020). Studying behaviour change mechanisms under complexity [Preprint]. PsyArXiv.

Olthof, M., Hasselman, F., & Lichtwarck-Aschoff, A. (2020). Complexity In Psychological Self-Ratings: Implications for research and practice [Preprint]. PsyArXiv.

An important complementary perspective to complexity basics:

Siegenfeld, A. F., & Bar-Yam, Y. (2020). An Introduction to Complex Systems Science and Its Applications. Complexity, 2020, 6105872.

More resources on complexity:

  1. Mathews, K. M., White, M. C., & Long, R. G. (1999). Why Study the Complexity Sciences in the Social Sciences? Human Relations, 52(4), 439–462. [INTRO COMPLEXITY SCIENCE]
  2. Richardson, M. J., Kallen, R. W., & Eiler, B. A. (2017). Interaction-Dominant Dynamics, Timescale Enslavement, and the Emergence of Social Behavior. In Computational Social Psychology (pp. 121–142). New York: Routledge. [INTERACTION-DOMINANCE]
  3. Molenaar, P. C., & Campbell, C. G. (2009). The new person-specific paradigm in psychology. Current directions in psychological science, 18(2), 112-117. [ERGODICITY]
  4. Kello, C. T., Brown, G. D., Ferrer-i-Cancho, R., Holden, J. G., Linkenkaer-Hansen, K., Rhodes, T., & Van Orden, G. C. (2010). Scaling laws in cognitive sciences. Trends in cognitive sciences, 14(5), 223-232. [SCALING PHENOMENA]
  5. Lewis, M. D. (2000). The promise of dynamic systems approaches for an integrated account of human development. Child development, 71(1), 36-43. [STATE SPACE, DYNAMICS]
  6. Olthof, M., Hasselman, F., Strunk, G., van Rooij, M., Aas, B., Helmich, M. A., … Lichtwarck-Aschoff, A. (2019). Critical Fluctuations as an Early-Warning Signal for Sudden Gains and Losses in Patients Receiving Psychotherapy for Mood Disorders. Clinical Psychological Science, 2167702619865969. [DYNAMIC COMPLEXITY]
  7. Olthof, M., Hasselman, F., Strunk, G., Aas, B., Schiepek, G., & Lichtwarck-Aschoff, A. (2019). Destabilization in self-ratings of the psychotherapeutic process is associated with better treatment outcome in patients with mood disorders. Psychotherapy Research, 0(0), 1–12. [DYNAMIC COMPLEXITY]
  8. Richardson, M., Dale, R., & Marsh, K. (2014). Complex dynamical systems in social and personality psychology: Theory, modeling and analysis. In Handbook of Research Methods in Social and Personality Psychology (pp. 251–280). [INTRO COMPLEXITY SCIENCE – Social and personality psychology]
  9. Wallot, S., & Leonardi, G. (2018). Analyzing Multivariate Dynamics Using Cross-Recurrence Quantification Analysis (CRQA), Diagonal-Cross-Recurrence Profiles (DCRP), and Multidimensional Recurrence Quantification Analysis (MdRQA) – A Tutorial in R. Frontiers in Psychology, 9. [MULTIDEMINSIONAL RQA]
  10. Webber Jr, C. L., & Zbilut, J. P. (2005). Recurrence quantification analysis of nonlinear dynamical systems. In Tutorials in contemporary nonlinear methods for the behavioral sciences (pp. 26–94). Retrieved from [RQA]
  11. Marwan, N. (2011). How to avoid potential pitfalls in recurrence plot based data analysis. International Journal of Bifurcation and Chaos, 21(04), 1003–1017. [RQA parameter estimation]
  12. Boeing, G. (2016). Visual Analysis of Nonlinear Dynamical Systems: Chaos, Fractals, Self-Similarity and the Limits of Prediction. Systems, 4(4), 37. [LOGISTIC MAP, DERTERMINISTIC CHAOS]
  13. Kelty-Stephen, D. G., Palatinus, K., Saltzman, E., & Dixon, J. A. (2013). A Tutorial on Multifractality, Cascades, and Interactivity for Empirical Time Series in Ecological Science. Ecological Psychology, 25(1), 1–62. [MULTI-FRACTAL ANALYSIS]
  14. Kelty-Stephen, D. G., & Wallot, S. (2017). Multifractality Versus (Mono-) Fractality as Evidence of Nonlinear Interactions Across Timescales: Disentangling the Belief in Nonlinearity From the Diagnosis of Nonlinearity in Empirical Data. Ecological Psychology, 29(4), 259–299. [(MULTI-)FRACTAL ANALYSIS]
  15. Hawe, P. (2015). Lessons from Complex Interventions to Improve Health. Annual Review of Public Health, 36(1), 307–323.
  16. Rickles, D., Hawe, P., & Shiell, A. (2007). A simple guide to chaos and complexity. Journal of Epidemiology & Community Health, 61(11), 933–937. [INTRO COMPLEXITY SCIENCE – Public health]
  17. Pincus, D., Kiefer, A. W., & Beyer, J. I. (2018). Nonlinear dynamical systems and humanistic psychology. Journal of Humanistic Psychology, 58(3), 343–366.  [INTRO COMPLEXITY SCIENCE – Positive psychology]
  18. Gomersall, T. (2018). Complex adaptive systems: A new approach for understanding health practices. Health Psychology Review, 0(ja), 1 – 34. [INTRO COMPLEXITY SCIENCE – Health psychology]
  19. Nowak, A., & Vallacher, R. R. (2019). Nonlinear societal change: The perspective of dynamical systems. British Journal of Social Psychology, 58(1), 105-128. [INTRO COMPLEXITY SCIENCE – Societal change]
  20. Carello, C., & Moreno, M. (2005). Why nonlinear methods. In Tutorials in contemporary nonlinear methods for the behavioral sciences (pp. 1–25). Retrieved from [INTERACTION DOMINANCE, ERGODICITY]
  21. Liebovitch, L. S., & Shehadeh, L. A. (2005). Introduction to fractals. In Tutorials in contemporary nonlinear methods for the behavioral sciences (pp. 178–266). Retrieved from [FRACTAL ANALYSIS]

Pathways and complexity in behaviour change research

These are slides of a talk given at the Aalto University Complex Systems seminar. Contrasts two views to changing behaviour; the pathway view and the complexity view, the latter being at its infancy. Presents some Secret Analysis Arts of Recurrence, which Fred Hasselman doesn’t want you to know about. Includes links to resources. If someone perchance saw my mini-moocs (1, 2) and happened to find them useful, drop me a line and I’ll make one of this.

Lifestyle factors are hugely relevant in preventing disease in modern societies; unfortunately people often fail in their attempts to change health behaviour – both their own, as well as that of others’. In recent years, behaviour change design has been conceived as a process where one identifies deficiencies in factors influencing the behaviours (commonly called “determinants”). Complexity thinking suggests putting emphasis on de-stabilisation instead.

The perspective taken here is mostly at the idographic level. At the time of writing, we have behaviour change methods to affect e.g. skills, perceived social norms, attitudes and so forth – but very little on general de-stabilisation of the motivational system as an important predictor of change.

Perspectives are welcome!

ps. Those of you to worry about brainwashing and freedom of thought: Chill. Stuff that powerful doesn’t really exist, and if it did, marketers would know about it and probably rule the world. [No, they don’t rule the world, I’ve been there]

pps. Forgot to put it in the slides, but this guy Merlijn Olthof will perhaps one day tweet about his work about destabilisation in psychotherapy contexts. Meanwhile, you can e.g. be his 10th Twitter follower, or keep checking his Google Scholar profile, as there’s a new piece coming out soon!

Randomised experiments (mis?)informing social policy in complex systems

In this post, I vent about anti-interdisciplinarity, introduce some basic perspectives of complexity science, and wonder whether decisions on experimental design actually lead us to end up in a worse place than where we were, before we decided to use experimental evidence to inform social policy.

People in our research group recently organised a symposium, Interdisciplinary perspectives on evaluating societal interventions to change behaviour (talks watchable here), as part of a series called Behaviour Change Science & Policy (BeSP). The idea is to bring together people from various fields from philosophy to behavioural sciences, medicine and beyond, in order to better tackle problems such as climate change and lifestyle diseases.

One presentation touched upon Finland’s randomised controlled trial to test the effects of basic income on employment (see also report on first year results). In crude summary, they did not detect effects of free money on finding employment. (Disclaimer: They had aimed for 80% statistical power, meaning that if all your assumptions regarding the size of the effect are correct, in the long term, 20% of the time you’d get no statistically significant effect in spite of there being a real effect.)

During post-symposium drinks, I spoke with an economist about the trial. I was wondering, how come they used individual instead of cluster randomisation – randomising neighbourhoods, for example. The answer was resource constraints; much larger sample sizes are needed for the statistics to work. To me it seemed clear, that it’s a very different situation if one person in a network of friends got free money, as compared to if everyone did. The economist wondered: “How come there could be second-order effects when there were no first-order effects?” The conversation took a weird turn. Paraphrasing:

Me: Blahblah compelling evidence from engineering and social sciences to math and physics that “more is different”, i.e. phenomena play out differently depending on the scale at consideration… blahblah micro-level interactions create emergent macro-level patterns blahblah.

Economist: Yeah, we’re not having that conversation in our field.

Me: Oh, what do you mean?

Economist: Well, those are not things discussed in our top journals, or considered interesting subjects to research.

Me: I think they have huge consequences, and specifically in economics, this guy in Oxford just gave a presentation on what he called “Complexity economics“. He had been doing it for some decades already, I think he originally had a physics background…

Economist: No thanks, no physicists in my economics.

Me: Huh?

Economist: [exits the conversation]

Now, wasn’t that fun for a symposium on interdisciplinary perspectives.

I have a lot of respect for the mathematical prowess of economists and econometricians, don’t get me wrong. One of my favourites is Scott E. Page, though I only know him due to an excellent course on complexity (also available as an audio book). I do probably like him, because he breaks out of the monodisciplinary insulationist mindset economists are often accused of. Page’s view of complexity actually relates to our conversation. Let’s see how.

First off, he describes complexity (and most social phenomena of interest) as arising from four factors, which can be thought as tuning knobs or dials. Complexity arises, when each dial is not tuned into either of the extremes, which is where equilibria arise, but somewhere in the middle. And complex systems tend to reside far from equilibrium, permanently.

To dig more deeply into how the attributes of interdependence,
connectedness, diversity, and adaptation and learning generate
complexity, we can imagine that each of these attributes is a dial that
can be turned from 0 (lowest) to 10 (highest).

Scott E. Page

  • Interdependence means the extent of how much one person’s actions affect those of another’s. This dial ranges from complete independence, where one person’s actions do not affect others’ at all, to complete dependence, where everyone observes and tries to perfectly match all others’ actions. In real life, we see both unexpected cascades (such as the US decision makers’ ethanol regulations, leading to the Arab Spring), as well as some, but never complete, independence – that is, manifestations that do not fit into either extreme of the dial, but lie somewhere in between.
  • Connectedness refers to how many other people a person is connected to. The extremes range from people living in a cabin in the woods all alone, to hypersocial youth living in Instagram trying to keep tabs on everyone and everything. The vast majority of people lie somewhere in between.
  • Diversity is the presence of qualitatively different types of actors: If every person is a software engineer, mankind is obviously doomed… But the same happens if there’s only one engineer, one farmer etc. Different samples of real-world social systems (e.g. counties) consist of intermediate amounts of diversity, lying somewhere in between.
  • Adaptation and learning refer to the extent of the actors’ smartness. This ranges from following simple, unchanging rules, to being perfectly rational and informed, as assumed in classical economics. In actual decision making, we see “bounded rationality”, reliance on rules of thumb and tradition, as well as both optimising and satisficing behaviours – the “somewhere in between”.

The complexity of complex systems arises, when diverse, connected people interact on the micro-level, and by doing so produce “emergent” macro-level states of the world, to which they adapt, creating new unexpected states of the world.

You might want to read that one again.

Back to basic income: When we pick 2000 random individuals around the country and give them free money, we’re implicitly assuming they are not connected to any other people, and/or that they are completely independent the actions of others’. We’re also assuming that they are either the same, or that it’s not interesting that they are of different types. And so forth. If we later compare their employment data to that of those who were not given basic income, the result we get is an estimate of the causal effect in the population, if all assumptions would hold.

But consider how these assumptions may fail. If the free money was perceived as a permanent thing, and given to people’s whole network of unemployed buddies, it seems quite plausible that they would adapt their behaviour as a response of the dynamics of their social network changing. This might even be different in cliques of certain people, who might use the safety net of basic income to collectively found companies and take risks, and cliques of other people, who might alter their daily drinking behaviour to match the costs with the predictable income – for better or worse. But when you randomise individually and ignore how people cluster in networks, you’re studying a different thing. Whether it’s an interesting thing or a silly thing, is another issue.

Now, it’s easy to come up with these kinds of assumption-destroying scenarios, but a whole different ordeal to study them empirically. We need to simplify reality in order to deal with it. The question is this: How much of an abstraction can a map (i.e. a model in a research study, making those simplified assumptions) be, in order to still represent reality adequately? This is also an ontological question, because if you take the complexity perspective seriously, you say bye-bye to the kind of thinking that allows you to dream up predictable effects a button-press (such as a policy change) has over the state of a system. People who act in—or try steering—complex systems, control almost nothing but influence almost everything.

An actor in a complex system controls almost nothing but influences almost everything.

Scott E. Page

Is some information, some model, still better than none? Maybe. Maybe not. In Helsinki, you’re better off without a map, than with a map of Stockholm – the so-called “Best map fallacy” (explained here in detail). Rare, highly influential events drive the behaviour of complex systems: the Finnish economy was not electrified by average companies starting to sell more, but by Nokia hitting the jackpot. And these events are very hard, if not impossible, to predict✱.

Ok, back to basic income again. I must say that the people who devised the experiment were not idiots, and included e.g. interviews of people to get some idea about unexpected effects. I think that this type of an approach is definitely necessary when dealing with complexity, and all social interventions should include qualitative data in their evaluation. But, again, unless the unemployed don’t interact, with randomisation done individually you’re studying a different thing than when it’s done in clusters. I do wonder if it would have been possible to include some matched clusters, to see if any qualitatively different dynamics take place, when you give basic income to a whole area instead of randomly picked individuals within it.

Complex systems organizational map.jpg
The society is a complex system, and must be studied as such. Figure: Hiroki Sayama (click to enlarge)

But, to wrap up this flow of thought, I’m curious if you think it is possible to randomise a social intervention individually AND always keep in mind that the conclusions are only valid if there are no interactions between people’s behaviour and that of their neighbours. Or is it inevitable that that the human mind smoothes out the details?

Importantly: Is our map better now, than it was before? Will this particular experiment go in history as—like the economist stated in “there were no first-order effects”—basic income not having any effect on job seeking? (remember, aim was only 80% statistical power). Lastly, I want to say I consider it unforgiveable to only work within one discipline and disregard the larger world you’re operating in: When we bring science to policy making, we must be doubly cautious of the assumptions our conclusions stand on. Luckily, transparent scientific methodology allows us to be explicit about them.

Let me hear your thoughts, and especially objections, on Twitter, or by email!

✱ One solution is to harness convexity, which can be oversimplified like this:

  1. Unpredictable things will happen, and they will make you either better or worse off.
  2. Magnitude of an event is different from it’s effect on you, i.e. there are huge events that don’t impact you at all, and small events that are highly meaningful to you. Often that impact depends on the interdependence and connectedness dials.
  3. To an extent, you can control the impact an event has on you.
  4. You want to control exposure in such a way, that surprise losses are bounded, while surprise gains are as limitless as possible.

Complexity considerations for intervention (process) evaluation

For some years, I’ve been partly involved in the Let’s Move It intervention project, which targeted dysfunctional physical activity and sedentary behaviour patterns of older adolescents, by affecting their school environment as well as social and psychological factors.

I held a talk at the closing seminar; it was live streamed and is available here (on stage starting from about 1:57:00 in the recording). But if you were there, or are otherwise interested in the slides I promised, they are now here.

For a demonstration of non-stationary processes (which I didn’t talk about but which are mentioned in these slides), check out this video and an experimental mini-MOOC I made. Another blog post touching on some of the issues is found here.


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