When Finland Decided to Open Up: A Behavioural Scientist’s Perspective

I’ve been writing this post between 4.-21. February 2022, when questionable information is rampant in Finnish public discussions: Omicron is thought to be comparable to the flu, a trend of ever-milder variants is considered an inevitable biological law, the pandemic is (again) claimed to be over, and the sentiment is that there’s nothing much we can or need do about it anyway. This post is a historical reference so we don’t forget what happened to a relatively successful Finnish pandemic management scheme between June 2021 and February 2022. I also present two future scenarios to consider, when the hopium in current media discourse turns out to be hot air again. My hope is, that individuals and communities will shift their perspective and start buffering the nation against bad outcomes, not the best-case scenarios – which do not require preparation anyway. The post does not reflect views of the behavioural science advisory group (KETTU) operating in the Finnish Prime Minister’s Office, nor those of the Citizen Shield pandemic prevention project.

Hi. We’re Finland. You may remember us from hit songs such as Don’t Stand So Close to Me, Unless We’re in the Sauna and Nokia’s Gonna Last Forever. In more recent times, we’ve been cited as one of the best countries in pandemic management: in fact, by fall 2021 had only had approximately one thousand recorded COVID-deaths. Reasons for the relative success include remote geographical location, low population density, and cultural features, including the general avoidance of close physical proximity between individuals – and perhaps less so public health prowess (in fact, we only narrowly avoided going Full Tegnell on the pandemic). Let me just quickly recount what happened to the success story in the latter half of 2021, because it’s kinda funny.

It’s June 2021, right before government officials start their holidays. I’m telling people we should make preparations for the Delta variant. The suggestion is shrugged off with humour, and in all fairness, it did look like we were going to near-zero cases again for the summer… Before a bunch of football fans returning from Russia kicked off the Delta wave. After the usual debate on whether new variants will find Finns attractive, cases started going up, and we were soon in the slow initial growth of the exponential curve. So, there I was in August 2021 when the holidays ended: anxiously awaiting for someone to state, that in order for us to keep the freedoms we had during the summer, we’re going to need to smash the curve (something akin to what various countries from Portugal to Japan later did). Instead, in early September, the government announced everyone has a great need to go back go normal, and that now is the time to do it: “It’s Time to Live!”

Our health officials agreed, because after all, Denmark had already decided, that September 2021 was the time to end precautions*. To quote our director of health security: “We are on the home stretch of the pandemic […] and due to the vaccination rate, don’t need to worry about the pandemic in Finland any more [although minor setbacks may occur]” (September 2021). Also in September, in a particularly funny episode, an epidemiologist from the Finnish equivalent of CDC, called a multidisciplinary group of local precaution-advocating scientists “a crackpots’ day care club”.

*footnote: some may forget, that this did not turn out very well for them or any other of the “we’re going to live with the virus now” countries – Denmark’s end of the pandemic was cancelled, until announced again in early 2022.

Here’s some survey data to describe the Finnish situation from a perspective of personal protective behaviours:

Figure 1: Despite the late adoption of masks, Finns rapidly increased their uptake in Fall 2020. Precautions decline during Fall 2021, under heavy mainstream media pressure to return to normal, and go up again as the situation worsens rapidly.

Major news media seemed convinced not only that the pandemic was over (again), but that they needed to perform psychological interventions to reduce the public’s risk perception – despite rising case numbers. Reporters were calling me on the phone: “You’re a social psychologist; how can we make people go back to normal?”. Chief editors of the largest newspapers made flashy statements such as “COVID is over when everyone stops being afraid”, and “[despite scientists who called for unnecessary suppression] Finland is nearing herd immunity, while Australia and New Zealand are in trouble” (aside: they weren’t, but we were).

The need to go back to normal was couched in statements about the people being tired of protective actions. This was curious, because all the while surveys indicated that people actually thought it was pretty important to continue exercising precaution, even after vaccinations:

Figure 2. Rising trend in stated need to continue precautions from mid-2021 to early 2022 – meanwhile, officials repeat the statement that people are very tired. Note: the behavioural science advisory group combined survey and interview data, concluding in a report in December, that people (mostly younger age groups) are mostly annoyed of restricting close contacts, while e.g. masks in public spaces remained highly acceptable.

In the end of November 2021, Omicron caught us in the midst of an exponentially worsening delta epidemic, and by the time of writing (February 2022), we are waiting for BA2 close to the worst situation of the pandemic’s history. Here’s a summary of what happened when the pandemic management strategy was changed in September 2021, to cater the people’s supposed need to go back to 2019 ASAP:

Figure 3. After Finland switched its pandemic management approach (less testing, never mind total case numbers, aim to go back to normal), things went somewhat awry.

What are we doing currently, to remedy the situation? Surely not repeating any past mistakes? Memorable bits of recent news: Our aim should be to open up quickly, COVID will be just like the flu by summer 2022Vaccinations and a weakening virus are making the epidemic lose its edge. Central assumptions – I repeat, assumptions – include that everyone will get sick, the population becomes stronger with better immunity, and after some time (One year? Ten years? Hundreds / thousands of years?), we don’t need to mind the virus any longer. Seeing our new director of the Ministry of Social Affairs and Health cite the inevitability of getting ill, it just makes me wonder: What none of this is true, who has a Plan B? To a non-doctor, long term neurological and cardiovascular damage does not look like something to aim for, in order to gain short-lived immunity. Also, many public officials in Southern Finland have ignored the law which says you should not let dangerous infectious diseases run amok – does this mean they now have a mental investment in the nothing-could-nor-can-be-done narrative?

If you’re Finnish, you may be suprised that many countries around the world are way better vaccinated than us, and are doing quite a good job in pushing down Omicron with vaccines and protective public health measures. The situation reflects the WHO Director General’s statement calling for a “vaccines-plus” -strategy:

I need to be very clear: vaccines alone will not get any country out of this crisis. Countries can and must prevent the spread of Omicron with measures that work today. It’s not vaccines instead of masks, it’s not vaccines instead of distancing, it’s not vaccines instead of ventilation or hand hygiene. Do it all. Do it consistently. Do it well.

– Director General of the WHO, Tedros Adhanom Ghebreyesus, as quoted in Covid-19: An urgent call for global “vaccines-plus” action 

I end with two long-term scenarios, just to record what I considered the most important at the time of writing:

An optimistic scenario. There’s some critical threshold of disability caused by the pandemic, as well as global supply chain malfunctions from the ever-increasing pace of immunity evading variants. Upon crossing this threshold, health officials, the business sector and/or government representatives understand, that the only way out is to go for the exit – i.e. an elimination strategy. It can be done, but not if you insist traditional epidemiological models are the reality, instead of just ways to simplify some part of it. There’s a paper coming out, which outlines how elimination can be achieved with mass testing; will add link when it’s out. [Note that it is clearly disinformation to state, that talking about elimination means advocating for an eternal lockdown.] Depending on your belief structure, the elimination strategy may seem far-fetched right now, but nonlinear impacts of not doing it can accumulate fast, if and when the pandemic continues its course instead of going away. 

A pessimistic scenario. Finnish Health officials await for a scientific consensus, which keeps proceeding at the pace of Planck’s principle (i.e. every step forward requires the funeral of an influential person keen on retaining the status quo). People “Swedify” and grow complacent, quick to normalise every new turn to the worse. We go back to accepting mortality rates of the pre-antibiotic era, until a new type of vaccine is created, which handles all potential variants. The vaccine is refused by some 5% of the population, from which forms the basis of a polarised societal system. In this system, the official services which refuse the unvaccinated, are replaced by underground / black market equivalents catering to their needs. The default option for countering the next pandemic becomes wait for the vaccine because we learned that anything else is ridiculous and a conspiracy theory. Then, while twiddling our thumbs, the next pandemic kills a billion people, which due to global interconnectedness, permanently changes the lives of several generations.

Tell me I’m wrong.

[with thanks to Kaisa Saurio for helpful comments]

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: www.mattiheino.com/besp.

Pandemia, irtikytkeytymiskyvykkyys ja mitä sitten?

This post lays out, in a nutshell, why I care about pandemics and how I think we should reasonably treat them.

Päivitys 23.12.2021: Alkuperäinen kirjoitus oli kesäkuussa 2021. Kirjoitus tarkastettu tällä päivämäärällä, ja todettu entistäkin ajankohtaisemmaksi (päivitetty ainoastaan Olet tässä -kuva).

Kysymys: Miksi pandemioista pitäisi välittää?

Vastaus: Tiedämme, että historiallisesti pandemioiden kuolonuhrit noudattavat äärimmäisen paksuhäntäistä todennäköisyysjakaumaa (esim. GPD-jakaumasta* todennäköisyys yli miljardin kuolonuhrin pandemialle ~1%).

(*parametrit: xi = 1.62, mu = 0, beta = 1.1747 * 10^6; ks. tämä)

Tiedämme myös, että riskiarvio on alakanttiin, koska globaali verkostoituneisuus on viimeisen 100 vuoden aikana lisääntynyt räjähdysmäisesti. Tämä lisää erilaisten dominoefektien todennäköisyyttä, jolloin seuraukset voivat olla tuhoisat (ks. esim. tämä tai tämä). Koronapandemia oli tätä taustaa vasten erinomainen valmiusharjoitus, sillä se ei pyyhkinyt suurta osaa ihmiskuntaa planeetalta – mikä on pitkällä tähtäimellä paitsi mahdollista, myös todennäköistä, mikäli kaikkialta pääsee kaikkialle kaiken aikaa (ks. tämä).

Kysymys: Mitä tarkoitat, kun sanot “vanha normaali”?

Vastaus: Tarkoitan 1900-luvun loppupuolella kehittynyttä mielialaa, jossa ajateltiin, että 1) pandemioista ei tarvitse välittää ja 2) naiivi skientismi on (epävarmuustekijöiden myöntämisen ja kontrolloinnin sijaan) ratkaisu käytännössä kaikkiin ihmiskuntaa kohtaaviin ongelmiin.

Kysymys: Mitä tarkoitat, kun sanot “uusi normaali”?

Vastaus: Tarkoitan tilannetta, jossa yhteiskunnan jatkuvuus varmistetaan, jotta voidaan nauttia saavutetuista eduista kuten vapaudesta, terveydestä ja hyvästä elämästä.

Kysymys: Miltä tämä sitten näyttäisi?

Vastaus: Jokapäiväinen elämä näyttäisi samalta kuin vuonna 2019, lukuunottamatta sitä, että aggressiivisten tartuntatautien esiintymisalueille matkustavien tulisi palatessaan käydä luotettavassa testissä tai viettää aikaa esim. karanteenihotellissa. Meillä olisi lisäksi arsenaalissamme irtikytkeytymiskyvykkyyttä: uusien, vakavien patogeenien ilmaantuessa jossain päin maailmaa, voisimme pystyttää palomuureja globaalin virusverkoston oville. Tämä pitäisi voida tehdä mahdollisimman vähän ihmisten elämää ja yhteiskunnan toimintoja häiriten – tavoitteena olisi, ettei se näkyisi millään tapaa jokapäiväisessä elämässä (rajojen yli säännöllisesti esim. työnsä vuoksi liikkuvat voisivat jatkaa liikkumista haettuaan siihen luvan).

Irtikytkeytyminen olisi aina väliaikaista ja sitä lyhyempää, mitä nopeammin uhkaavan patogeenin riskiarviointi saataisiin tehtyä. Sen hyväksyttävyyttä tulisi arvioida kuuntelemalla kaikkia kansan sosioekonomisia luokkia: ei ole lainkaan itsestäänselvää, että varakkaiden tulisi voida matkustaa (aina, kaikkialle, ja kaiken aikaa), mikäli se tarkoittaa haavoittuvampien ryhmien elämän merkittävää häiriintymistä (esim. kirjastojen ja muiden julkisten palveluiden sulku, tulonmenetykset ja vaikeasti toteutettavat etätyövaatimukset, jne.)

Käytännössä tämä tarkoittaisi hälytysjärjestelmää, jonka lauetessa testausta ja karanteeneja otettaisiin käyttöön mieluummin liian nopeasti kuin liian hitaasti, sillä mikäli järjestelmä pettää ja uusi patogeeni pääseekin maahan, kansalaisten elämä häiriintyy mahdollisesti hyvinkin merkittävästi (ks. vuoden 2020 keväällä alkanut sulkujojoilu läntisellä pallonpuoliskolla). Toisin sanoin: liikkumis- ja muut rajoitukset maan sisällä ovat seurausta rajatoimien epäonnistumisesta.

Liikkumis- ja muut rajoitukset maan sisällä ovat seurausta rajatoimien epäonnistumisesta.

Atlantinpuolisessa Kanadassa, Australiassa ja Uudessa-Seelannissa on viimeisen reilun vuoden aikana kehitetty toimintamalleja tämän toteuttamiseksi, ja aiheesta opitaan koko ajan lisää. Suomesta koronavirus on käytännössä eliminoitu useita kertoja – muualla maassa tosin HUS:ia tehokkaammin – joten tähän on meillä moniin maihin nähden hyvät edellytykset. Rajoituksia täälläkin on kuitenkin jouduttu jatkamaan, koska niiden aiheuttama vaivannäkö on ehkä arvioitu pienemmäksi, kuin rajojen terveysturvallisuuden turvaamisen aiheuttama vaivannäkö.

Kysymys: Kuulostaa kamalan hankalalta ja meillähän on jo rokote?

Vastaus: Olen huolissani neljästä asiasta, mikäli eliminaatio-/segmentaatiostrategiaa ei oteta käyttöön, tai irtikytkeytymiskyvykkyyttä aleta pikimmiten rakentamaan:

  1. Uudet variantit. Kuinka kauan haluamme tanssia uusien boosteri- ja kausirokotusten kanssa toivoen, että sellainen voittaa kilpajuoksun uutta virusvarianttia vastaan?
  2. Pitkä COVID. Mitä riskeeraamme altistamalla rokottamattomat ihmisryhmät taudille?
  3. Kansalaisvaltioiden lisääntynyt riippuvuus lääkeyhtiöjättiläisistä. Onko se toivottavaa?
  4. Seuraava pandemia. Jos emme muuta mitään, kuinka paljon haluamme lyödä vetoa sen puolesta, että saamme ensi kerralla rokotteen ennen kuin korjaamaton vahinko on tapahtunut?

Kuten THL:n seminaarissa esitin, meidän tulisi tehdä kansana päätöksiä siitä, millaisia arvoja haluamme toteuttaa pandemiauhkien kanssa toimiessa. Kun tämä on yhdessä sovittu, pienemmille yksiköille (AVIt, maakunnat, kaupungit, kylät, naapurustot, perheet, yksilöt) täytyy antaa autonomiaa toteuttaa koonsa puolesta omaan vaikutuspiirinsä kuuluvia käytännön torjuntatoimien toteutuksia, omia vahvuuksiaan hyödyntäen. Mutta parasta olisi, jos proaktiivinen ja tehokas viranomaisvaste pääosiltaan poistaisi kansalaisten tarpeen käyttää omia resurssejaan tartuntataudin selättämiseksi.

Haluan korostaa, että tämä ei ole mikään “Rajatkii!”-sotahuuto. Esimerkiksi verkostotieteen pohjalta voidaan kylmän viileästi tehdä johtopäätös, että resilienttien järjestelmien osat eivät ole kaikki yhteydessä toisiinsa kaiken aikaa (ks. esim. tämä). Mika Salmisen sanoin: Eihän korona itsestään mistään ilmesty, vaan rajojen yli se tulee.

Kiinnostuneille tutkimusviitteitä alla.

Tässä vielä 3,5 minuuttia pähkinänkuorta siitä, mitä “laipiointi” eliminaatio- / segmentaatiostrategiassa tarkoittaa. Video on pätkä Arjen Resilienssi -webinaarista; sanon siinä tartuntojen olevan menossa kohti nollaa, koska tapahtuma oli ennen kesäkuun lopun deltaorgioita.

Lukemistoa:

  • Baker, M. G., Wilson, N., & Blakely, T. (2020). Elimination could be the optimal response strategy for covid-19 and other emerging pandemic diseases. BMJ, 371, m4907. https://doi.org/10/ghqk9h
  • Balsa-Barreiro, J., Vié, A., Morales, A. J., & Cebrián, M. (2020). Deglobalization in a hyper-connected world. Nature Palgrave Communications, 6(1), 1–4. https://doi.org/10/gjfxwz
  • Flyvbjerg, B. (2020). The law of regression to the tail: How to survive Covid-19, the climate crisis, and other disasters. Environmental Science & Policy, 114, 614–618. https://doi.org/10/gjkjwz
  • Hansson, S. O. (2004). Fallacies of risk. Journal of Risk Research, 7(3), 353–360. https://doi.org/10/c7567q
  • Horton, R. (2021). Offline: The case for No-COVID. Lancet397(10272), 359. https://doi.org/10.1016/S0140-6736(21)00186-0
  • Hyvönen, A.-E., Juntunen, T., Mikkola, H., Käpylä, J., Gustafsberg, H., Nyman, M., Rättilä, T., Virta, S., & Liljeroos, J. (2019). Kokonaisresilienssi ja turvallisuus: Tasot, prosessit ja arviointi [Raportti]. Valtioneuvoston kanslia. https://julkaisut.valtioneuvosto.fi/handle/10024/161358
  • Iwata, K., & Aoyagi, Y. (2021). Elimination of covid-19: A practical roadmap by segmentation. BMJ, n349. https://doi.org/10/gjqpxt
  • Käyttäytymistieteellisen neuvonantohankkeen työryhmä. (2021). Vaikuttavat valinnat päätöksenteon tukena: Käyttäytymistieteellinen neuvonanto -hankkeen loppuraportti [Sarjajulkaisu]. Valtioneuvoston kanslia. https://julkaisut.valtioneuvosto.fi/handle/10024/163138
  • Matti TJ Heino, Markus Kanerva, Maarit Lassander, & Ville Ojanen. (2021). Koronaväsymystä? Vai inhimillistä kyllästymistä, turhautumista, tottumista ja pyrkimystä normaaliin (Käyttäytymistieteellisen neuvonantoryhmän raportteja). https://vnk.fi/hanke?tunnus=VNK127:00/2020
  • Martela, F., Hankonen, N., Ryan, R. M., & Vansteenkiste, M. (2020). Motivating Voluntary Compliance to Behavioural Restrictions: Self-Determination Theory–Based Checklist of Principles for COVID-19 and Other Emergency Communications. European Review of Social Psychology. 10.1080/10463283.2020.1857082
  • Morales, A. J., Norman, J., & Bahrami, M. (Toim.). (2021). COVID-19: A Complex Systems Approach. STEM Academic Press. https://stemacademicpress.com/stem-volumes-covid-19
  • Priesemann, V., Balling, R., Brinkmann, M. M., Ciesek, S., Czypionka, T., Eckerle, I., Giordano, G., Hanson, C., Hel, Z., Hotulainen, P., Klimek, P., Nassehi, A., Peichl, A., Perc, M., Petelos, E., Prainsack, B., & Szczurek, E. (2021). An action plan for pan-European defence against new SARS-CoV-2 variants. The Lancet, S0140673621001501. https://doi.org/10/ghtzqn
  • Priesemann, V., Brinkmann, M. M., Ciesek, S., Cuschieri, S., Czypionka, T., Giordano, G., Gurdasani, D., Hanson, C., Hens, N., Iftekhar, E., Kelly-Irving, M., Klimek, P., Kretzschmar, M., Peichl, A., Perc, M., Sannino, F., Schernhammer, E., Schmidt, A., Staines, A., & Szczurek, E. (2021). Calling for pan-European commitment for rapid and sustained reduction in SARS-CoV-2 infections. The Lancet, 397(10269), 92–93. https://doi.org/10/ghp8kb
  • Rauch, E. M., & Bar-Yam, Y. (2006). Long-range interactions and evolutionary stability in a predator-prey system. Physical Review E73(2), 020903. https://doi.org/10/d9zbc4
  • Siegenfeld, A. F., & Bar-Yam, Y. (2020). The impact of travel and timing in eliminating COVID-19. Nature Communications Physics, 3(1), 1–8. https://doi.org/10/ghh8hg
  • World Health Organization Regional Office for Europe. (2020). Pandemic fatigue: Reinvigorating the public to prevent COVID-19: policy considerations for Member States in the WHO European Region (WHO/EURO:2020-1160-40906-55390). Article WHO/EURO:2020-1160-40906-55390. https://apps.who.int/iris/handle/10665/335820

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:

 

The Power of Inflexibility in Improving Science and Fighting COVID-19

In case you’re new to this blog, you might not be aware of the ongoing crisis of confidence—also known as the Replication Crisis—in social and life sciences, including but not limited to psychology, medicine and economics. (To learn more, see weeks I-II of my course Critical Appraisal of Research Methods and Analysis.)

In short, major problems include:

  • Less than half (exact number depending on the field) of studies can be replicated
  • Way too few studies can be computationally reproduced, that is, getting the same results from the same data and same analysis code
  • Research tends to ignore context, making generalisability difficult
  • Published studies are reported intransparently, so it’s hard to tell what was actually done – and if p-hacking practices were used (e.g. the results were cherry picked from a large pool of random data)
  • … etc.

There are several initiatives to address these concerns, but where do they spring from, and how can we eventually fix science in large scale? I’m going to suggest a solution which will rub a lot of people the wrong way. Incidentally, it is the same tool we need to fight the Coronavirus. But first, we need to understand Nassim Taleb’s presentation of the minority rule.

The basic idea is, that under particular conditions, once a stubborn niche population reaches a small level such as 3-4% of the total population, the majority will have to submit to the preferences of the minority. For example, consider a children’s party, where the organiser needs to make the decision on whether to offer milk products, as some of the guests are lactose-intolerant. Let us call these the inflexible ones: They would suffer great harm from milk products, so they avoid them. The majority of the guests, the flexible ones, can consume both lactose-free products, as well as those which contain milk. Given that the lactose-free supplies are easily available and of not significantly inferior quality, it makes the organiser’s (as well as those party guests who are inflexible) life much easier to serve no milk products at all.

As another example, during my previous life as a business person, I did a degree where my peers were about 50% Finnish, and 50% other nationalities ranging all the way from Russia to Peru. Us Finns spoke Finnish with each other, but whenever a non-Finnish person entered the group, we switched to English. The proportion of non-Finnish speakers was irrelevant, whenever it was above 0%.

So, an inflexible minority can drastically affect how the majority acts. But the infexibility can also stem from one’s worldview; if you had to decide on a daytime activity with a bunch of friends during Ramadan, and one of them was Muslim, you wouldn’t go to a steak house.

What does this mean for improving science and weakening the Coronavirus?

  • In order to promote good research, transparency advocates need to be inflexible about questionable research practices. To the point that they lose potential career opportunities – although they may, in turn, gain better ones as they can work with likeminded people.
  • In order to smash COVID-19, citizens need to be inflexible about risk behaviours. To the point that some people consider them overzealous and rigid – although it may not matter, if it leads to surviving the crash.

Both of these causes have a very important fractal, or multiscale component: Much of the action is not top-down but happens bottom-up; the individual reels in their family (or immediate research group), who then become norm-setters in their apartment building/neighbourhood (or scientific society of their research area), who again affect local governance (or scientific discipline).

But there are at least three crucial success factors for the behaviour change effect to work:

  1. The inflexible group needs to be spatially spread widely, instead of being confined in particular geographic (or intellectual) pockets, in which case the majority can just isolate and ignore them.
  2. The cost of aligning with the inflexible group needs to be small for the flexible group. For minority members to change behaviour, therefore, it may be necessary to take up some of its costs to the majority – at least initially. The other option is to move steps that are so small they are almost imperceptible.
  3. Crucially, the inflexible group… Does. Not. Budge. People always tend to say that one “must not be so strict”, but there is a reason it is not okay to steal, murder, or cheat upon your spouse “just a bit”. If the inflexibles are perceived to be flexible, after all, the majority can expect to dominate over them.

no_stretching_alora-griffiths-WX7FSaiYxK8-unsplash
No rest for the wicked, and no stretching for the inflexible! (Photo: Alora Griffiths on Unsplash)

For our case examples, spatial spread is mostly taken care of: The internet has done much to allow for the minority members to connect, while being perhaps the only ones in their own immediate vicinity passionate about their cause. So I’ll address #2-#3.

Lowering the cost of transparency: In the scientific transparency scene, this means the minority representatives need to spend tons of time learning about transparent research practices (e.g. pre-registration and data sharing, the TOP Factor, etc.). This knowledge they can then either disseminate to the rest of their research group, or act as the person who does most of the heavy lifting required in reporting reproducible work.

Lowering the cost of Coronavirus safety: The anti-Coronavirus advocates, on the other hand, need to make information easily available (as they do in endcoronavirus.org), share it, and translate it – both literally and figuratively. An example would be sharing research studies, ways to make and wear masks correctly, or how to acquire them (if you’re in Finland, check this out to have masks made for you, while donating some to healthcare workers). They may also need to learn about technicalities of video conferencing and other solutions, so that they can readily teach their peers after refusing face-to-face meetings.

Not budging in research transparency: The research transparency people obviously need to refuse co-authoring papers which contain p-hacking, hyperbole or other ways of distorting the findings to improve chances of publication. They need to refuse projects which do not plan to share analysis code (and data, within privacy constraints), ask about transparency before peer reviewing, and walk away from papers where the first author insists on presenting exploratory hypotheses as confirmatory ones, or is not willing to properly discuss constraints to generalisability, model assumptions (stationarity, homogeneity, independence, interference, ergodicity… see here if these are strange words) and sensitivity analyses.

Not budging in Coronavirus safety: The anti-Coronavirus folks need show example by performing hand hygiene, self-isolating, wearing masks, social distancing, and taking their kids off school/daycare – but also making sure their family does the same. In addition, they need to speak out when they see their friends or neighbours acting out risk behaviours, such violating the 2-meter (6-feet) physical distance requirement. They need to make it clear they are only available for meetings via video conferencing, which they’re happy to help setting up.

Remaining steadfast and vocal is not for everyone, and calling out behaviour you perceive to be wrong, can be extremely anxiety-provoking. That’s also why one needs to start with those closest to them. And it is hard to be inflexible in the beginning, when the majority norms are against you and everyone is expected to play along. The “happy” news is, that not everyone needs to be inflexible – just the small minority. (I’m putting happy in quotes, because the minority rule can be leveraged to gradually promote any fascist ideology the majority is foolish enough to tolerate.)

Hence, if you’re the type of person who feels strongly enough to be inflexible about these things, perhaps you can feel comforted by the idea that you don’t need to convert the majority: The stubborn few can create the critical mass and change the world.

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

Literature:

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. https://doi.org/10.3389/fams.2020.00009

Heino, M. T. J., Knittle, K. P., Noone, C., Hasselman, F., & Hankonen, N. (2020). Studying behaviour change mechanisms under complexity [Preprint]. PsyArXiv. https://doi.org/10.31234/osf.io/fxgw4

Olthof, M., Hasselman, F., & Lichtwarck-Aschoff, A. (2020). Complexity In Psychological Self-Ratings: Implications for research and practice [Preprint]. PsyArXiv. https://doi.org/10.31234/osf.io/fbta8

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. https://doi.org/10.1155/2020/6105872

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. https://doi.org/10.1023/A:1016957424329 [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. https://doi.org/10.1080/10503307.2019.1633484 [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. https://doi.org/10.3389/fpsyg.2018.02232 [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 http://www.saistmp.com/publications/spiegorqa.pdf [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. https://doi.org/10.1142/S0218127411029008 [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. https://doi.org/10.3390/systems4040037 [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. https://doi.org/10.1080/10407413.2013.753804 [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. https://doi.org/10.1080/10407413.2017.1368355 [(MULTI-)FRACTAL ANALYSIS]
  15. Hawe, P. (2015). Lessons from Complex Interventions to Improve Health. Annual Review of Public Health, 36(1), 307–323. https://doi.org/10.1146/annurev-publhealth-031912-114421
  16. Rickles, D., Hawe, P., & Shiell, A. (2007). A simple guide to chaos and complexity. Journal of Epidemiology & Community Health, 61(11), 933–937. https://doi.org/10.1136/jech.2006.054254. [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. https://doi.org/10.1177/0022167817741784.  [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. https://doi.org/10.1080/17437199.2018.1488603. [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. https://doi.org/10.1111/bjso.12271. [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 https://nsf.gov/pubs/2005/nsf05057/nmbs/chap1.pdf [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 https://nsf.gov/pubs/2005/nsf05057/nmbs/chap5.pdf [FRACTAL ANALYSIS]

Suomenkielisiä työkaluja COVID-19 taisteluun; yksilöille, yrityksille ja päätöksentekijöille

This post curates Finnish translations (mostly NECSI guidelines) for stopping the Coronavirus pandemic. Tälle sivulle olen koonnut hyvinä pitämiäni suomenkielisiä tekstejä. Suomentajana Thomas Brand, ellei toisin mainita. Katso myös pandemioita pitkään tutkineen kompleksisuustieteilijä Yaneer Bar-Yamin haastattelu Suomen tilanteeseen liittyen.

Marraskuussa 2019 sain stipendin turvin mahdollisuuden osallistua Nassim Talebin riskinhallintaryhmän koulutukseen New Yorkissa. Siellä käsittelimme pandemiankaltaisia riskejä ja toimintaa niiden välttämiseksi. Muutamaa kuukautta myöhemmin pääsinkin elämään painajaista nähdessäni, että käytännössä kaikki länsimaat toimivat täysin vastoin varovaisuusperiaatetta (ts. joukkotuhon uhka on aina vältettävä agressiivisin toimin), luottaen “parhaaseen nykytietoon” viiveellä ilmenevän riskin torjumisen sijaan.

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Kokous, joka käytiin vuoden 2020 alussa jokaisessa maailman maassa. Lähde: xkcd

Alla hyviä kirjoituksia, jotka ovat pääosin alunperin NECSI-instituutin tuottamia.  NECSI:lla on pitkä historia hallitusten ja järjestöjen kuten WHO:n konsultoinnissa mm. Ebola ja Zikavirus-epidemioita nitistettäessä, mutta myös muissa kompleksisissa ongelmissa, joihin perinteinen matemaattinen mallinnus ei pure. Koronavirus-pandemiaan liittyvään vapaaehtoisten globaaliin verkostoon voi liittyä täältä; tekemistä on käännöksistä some-aktiviteettiin, maskien ompeluun, hengityslaitteiden suunnitteluun, verkkosivujen ja mobiilisovellusten luomiseen ym.!

Lyhyitä perusohjeistuksia:

Ehdotuksia henkilökohtaiselle toiminnalle tilanteen parantamiseksi:

Jos koet lieviä tai kohtalaisia oireita:

Jos osaat ommella, tai muuten luotat kätevyyteesi:

Ohjeita elinkeinoelämän toimijoille:

Ohjeita ja esseitä yhteiskunnallisille päättäjille:

Mallintamiseen, ennakointiin ja pandemiatutkimukseen liittyviä kirjoituksia

 

koronaohjeet