A study of tipping point: much less is known about the most efficient ways to reach such transitions or how self-reinforcing systemic transformations might be instigated through policy. We employ an agent-based model to study the emergence of social tipping points through various feedback loops that have been previously identified to constitute an ecological approach to human behavior. Our model suggests that even a linear introduction of pro-environmental affordances (action opportunities) to a social system can have non-linear positive effects on the emergence of collective pro-environmental behavior patterns.

1. NG2S: A Study of Pro-Environmental
Tipping Point via ABMs
Kan Yuenyong
Wednesday, March 17, 2021
GSPA
NIDA DA8420: Development Policy in Global Era

2. Based on
Kaaronen, R. O., and Stelkovskii, N. (2020).
Cultural Evolution of Sustainable Behaviors: Pro-
environmental Tipping Points in an Agent-Based
Model. In One Earth 2, pp. 85-97. DOI: 10.1016/
j.oneear.2020.01.003

3. Highlights
• An ABM is used to study the cultural evolution of sustainable behaviors
• Behaviors emerge as a function of affordances, social learning, and habits
• The affordances in an environment have a major effect on behavior
adoption
• The ABM is validated against cycling behaviors in Copenhagen
• A study of tipping point: much less is known about the most efficient
ways to reach such transitions or how self-reinforcing systemic
transformations might be instigated through policy. We employ an agent-
based model to study the emergence of social tipping points through
various feedback loops that have been previously identified to constitute
an ecological approach to human behavior. Our model suggests that even
a linear introduction of pro-environmental affordances (action
opportunities) to a social system can have non-linear positive effects on
the emergence of collective pro-environmental behavior patterns.

4. eScience: A New Trend in Scientific Research
http://jimgray.azurewebsites.net/talks/nrc-cstb_escience.ppt
“Jim Gray on eScience: A Transformed
Scientific Method.”
In: The Fourth Paradigm: Data-
Intensive Scientific Discovery. (2009).

5. • There are two terminologies that relate to each other:
Social Science Computing (SSC) and Computational
Social Science (CSS). The CSS is an interdisciplinary
between computer science, statistics, and the social
science, while the SSC will relate to apply
computational methodologies to help explaining social
phenomena. The CSS is “inherently collaborative:
social scientists provide vital context and insight into
pertinent research questions, data sources, and
acquisition methods, while statisticians and computer
scientists contribute expertise in developing
mathematical models and computational tools. New,
large-scale sources of demographic, behavioral, and
network data from the Internet, sensor networks, and
crowdsourcing systems augment more traditional data
sources to form the heart of this nascent discipline,
along with recent advances in machine learning,
statistics, social network analysis, and natural
language processing” (Mason, Vaughan and Wallach,
2014: 257-260)
CSS vs SSC

6. There are two factors to drive “radical” epistemic shift in this kind of cognitive computing that are: firstly, an attempt to understand
interdisciplinary problems such as climate change, “where the phenomena under investigation are spread across many time-scales and spatial
levels, and complex feedback loops are standard features of the domain,” and secondly, an emergence of “Big Data” generated routinely in
lab, particularly in biological sciences (Chandrasekharan and Nersessian, 2015: 1729)

9. From in-vivo & in-vitro -> in-silico & simulated (r)evolution
https://www.facebook.com/biologybeyondnature/photos/pcb.1861747900654448/1861746100654628

10. https://www.vice.com/en/article/59zj9b/craig-venters-digital-to-biological-converter-is-real
Printing “Life” from “Code”

11. https://www.vice.com/en/article/ae3pnp/elon-musk-and-craig-venter-want-to-print-life-on-mars

12. A spreading of computational simulation epistemology

13. An increasing in research and development expenditure by the U.S. federal government at USD 25.7 billion in life science as the highest sector in
2003 and a more complex value chain in biotechnology thus been regarded as an early signal of the sixth Kondratieff wave centered as
biotechnology (Nefiodow and Nefiodow, 2014 :339-343) seems to be too much mechanistic. If we take a look from the broader picture, as
cognitive revolution as a whole, this kind of increasing of investment and expenditure in the U.S. federal government will be spilled over to other
sectors either. The deep driver is, therefore, an exponential growth in computational power and a rising of neuromorphic machine.

14. https://www.statnews.com/2020/11/23/raging-at-the-pandemic-a-vc-
raises-800m-for-a-company-that-aims-to-transform-biotech-manufacturing/ https://resilience.com/capabilities/

15. DARPA’s Next Generation Social
Science (NGS2) and Ground Truth (GT)

16. https://www.darpa.mil/attachments/(2O15)%20Global%20Nav%20-%20About%20Us%20-%20History%20-%20Resources%20-
%2050th%20-%20Internet%20(Approved).pdf

17. CAS / Chaos Theory, Emergence and Feedback loop (both negative & positive)
https://www.gotoknow.org/posts/688786

18. https://www.quantamagazine.org/hidden-heroines-of-chaos-ellen-fetter-and-margaret-hamilton-20190520/

20. Why not?
1. 3rd world academia has to depend on 1st world research funding?
2. Mature epistemology
3. More investment poured in both from private startups and governmental expenditure
4. Speedy research cycle. Huge number of publications in outstanding academic journals, i.e. on
Biotech
5. More research communities all over the world
6. More commercial application and startups!
7. More speeding research findings -> Reinforcing on more publications -> Commercial startups (back
to #3 -> #4 -> #5 -> #6 -> #7 again and again).
8. The Paradigm Shift is clear!

21. Forget About It!
Norma M. Riccucci, “Public Administration: Traditions of Inquiry and Philosophies of Knowledge”, Georgetown University Press, 2010.

24. simulation epistemology = generative postpositivism?
• How relates between simulation and the subject (or the
target), “Simulations are also often used to learn about
something in the world. Yet here, as with models more
generally, the relation between simulation and target is
problematic. A lot depends on how the ‘target’ is
construed.” (Grüne-Yanoff and Weirich, 2010: 24). Actually,
“the target is not the data but the object or event itself (it may
be more appropriate to speak of phenomena here, in the
sense of Bogen & Woodward, 1988). Because phenomena do
not have an inherent mathematical structure, they cannot be
linked to simulations via isomorphism. Instead, the properties
that the simulation exemplifies (in what may be an imaginary
or fictional world) are compared to the properties instantiated
in the real-world situation. The relation between simulation
and phenomena is then explicated as a similarity relation
(Giere, 1988, 2004; Teller, 2001)” (cited in Grüne-Yanoff and
Weirich, 2010: 24). However, model is not simulation, “while
this broad ‘models as autonomous mediators’ view seems to
fit simulations rather well, one should also be aware of
important differences. One important difference is the
temporal dimension of simulations. Scientists often speak
about a model ‘underlying’ the simulation. The recent
smallpox infection simulation of Eubank et al. (2004)”, cited in
Grüne-Yanoff and Weirich, 2010: 25.)

25. Computer Modeling & Simulation, Cloud Computing, High Speed Network, HPC & Clusters, etc.
A computer simulation on computer notebook is considered amateur!

26. ENIAC (1946) and Monte Carlo simulation

27. https://vimeo.com/manage/361798743/general
Simple Simulation Enhanced Simulation
Reality
(Ground Truth)

28. Impacts of eScience’s replicability and reproducibility in software development: DevOps

29. Agent-Based Models
• the agent-based computational model permits a distinctive
approach to social science for which the term “generative” is
suitable. In defending this terminology, features distinguishing the
approach from both “inductive” and “deductive” science are
given.
• From an epistemological standpoint, generative social science,
while empirical, is not inductive, at least as that term is typically
used in the social sciences (e.g., as where one assembles
macroeconomic data and estimates aggregate relations
econometrically). (For a nice introduction to general problems of
induction, beginning with Hume, see Chalmers, 1982. On
inductive logic, see Skyrms, 1986. For Bayesians and their critics,
see, respectively, Howson and Urbach, 1993 and Glymour, 1980.)
• Agent-Based Modeling Incompatible with Classical Emergentism:
Before explicating the logical confusion noted by Hempel and
Oppenheim, we can fruitfully apply a bit of logic ourselves. Notice
that we have actually accumulated a number of first-order
propositions. For predicates, let C stand for classically emergent,
D for deducible, E for explained, and G for generated (in a
computational model). Then, if x is a system property, we have:
Thus, the motto of generative social science, if you will, is: If you
didn’t grow it, you didn’t explain its emergence. Or, in the
notation of first-order logic:
A relative version of emergence due to Hempel and Oppenheim (1948) is formalized in
Stephan (1992) as follows. Consider a system with constituents C1, . . . , Cn in relation
O to one another (analogous to Broad’s A, B, C, and R). “This combination is termed a
microstructure [C1, . . . Cn;O]. And let T be a theory. Then, a system property P is
emergent, relative to this microstructure and theory T, if:
(a.) There is a law LP which holds: for all x, when x has microstructure [C1, . . . Cn;O]
then x has property P, and
(b.) By means of theory T, LP cannot be deduced from laws governing the C1, . . . Cn
in isolation or in other micro- structures than the given.”

30. Affordance
• Kurt Lewin’s behavior equation is “B = f(P, E)”. It states that an individual’s behavior (B) is a
function (f) of the the person (P), including their history, personality and motivation, and their
environment (E), which includes both their physical and social surroundings.
• Kurt Lewin’s behavior model is very simple, but we think it elegant. Put simple is says that an
individual’s behavior is a function of that individual and their environment. Of course, we
acknowledge this model looks totally simple, so much so that it’s just common sense. We’re not
sure that it was at the time though.
• A chair, for instance, affords the function of sitting for humans, whereas a bicycle affords
cycling. Affordances are specified to an organism through the availability of ecological
information
• For instance, a bicycle path will only afford bicycling for a person who knows how to cycle. The
basic logical structure of an affordance can therefore be defined as ‘‘affords-f (environment,
organism), where f is a behaviour.’
• If one accepts that affordances are not properties of objects, it is a small step to see that
affordances cannot be properties, or even features, of the environment alone. I have just argued
that affordances are features of whole situations. Animals are, of course, crucial parts of these
whole situations, so perceiving something about the whole situation cannot be perceiving
something about the environment, divorced from the animal. Thus, as Stoffregen (2003)
suggests, affordances must belong to animal–environment systems, not just the environment.
Though I agree with Stoffregen on this point, I’d like to argue for something more specific: that
affordances are relations. To see this, consider Harry Heft’s (2001) discussion of the relation
between Gibson and the American naturalist William James

31. Many feedback loops

32. Many feedback loops

35. • See the Netlogo simulation!
• https://github.com/roopekaaronen/affordance

36. Strengths
• Follow the new paradigm
• Ground theory is clear (affordance)
• Enable to grasp emergence in simulation, instituted from various feedback
loops which equation cannot bear*** (normally we find “sig” in empirical
study)
• Is enable to study code and output step by step
• Verified with real world empirical data

37. Weaknesses
• It isn’t clear whether the anti-climate change can reverse the trend?
• Are there any other macro factor to dictate the trend? i.e. Global
awareness on climate change? Technological evolution?
• No clear extension from classic scientific postpositivism (not really a
weakness).
• Can it apply with other research question such as the paradigm shift in
social science research (not really a weakness).

39. GSPA
NIDA DA8420: Development Policy in Global Era
Q&A?
END