The document discusses the relationship between artificial intelligence (AI), heuristics, and human behavior, emphasizing the concept of ecological rationality and its applications in decision-making. It critiques traditional models of rationality and underscores the value of heuristics in uncertain environments, stating that heuristics can lead to effective decision-making in various fields. The content also touches on evolutionary computing and the potential future of AI as it approaches human-like intelligence through heuristic responses and ecological processes.

1. Gerard Rego
Why or why not AI? After AI what?
Homo-Heuristicus Evolutionary-Heuristics Singularity
All information presented is acknowledged and any data, information, images, etc. not acknowledged by sources is by oversight.

2. Gerard Rego
Artificial Intelligence 101, Fundamentally Flawed?
https://www.fer.unizg.hr/_download/repository/AI-1-Introduction.pdf

3. Gerard Rego
Human Behaviour is… Heuristics
AI
https://www.fer.unizg.hr/_download/repository/AI-1-Introduction.pdf
Heuristically
Maximizers/Satisficers Bounded Rationality/
Predictably Irrational
https://www.ethz.ch/content/dam/ethz/special-interest/gess/chair-of-sociology-dam/documents/icsd2013/0_3_gigerenzer.pdf
Ecological Rationality

4. Gerard Rego
4
EMT (Rational) – A Nobel Prize? Heuristics Suggest Otherwise
EMT (Heuristic)
https://www.nobelprize.org/prizes/economic-sciences/2013/fama/lecture/
Price reflects all Public information?
Strong Form(er When?)
No
No
Markets are Weak (Always)
Price reflects all Past information?
Semi-Strong Form(er When?)
Weak Form(All the Time!!!)
Price reflects all Public & Private?
information?
https://www.thisismoney.co.uk/money/markets/article-2369171/City-watchdog-targets-banks-Libor-fixing.html
https://www.treasury.gov/resource-center/data-chart-center/Documents/20120413_FinancialCrisisResponse.pdf
Eugene F.
Fama
The Sveriges Riksbank Prize in Economic
Sciences in Memory of Alfred Nobel 2013
Prize motivation: "for their empirical
analysis of asset prices."
https://www.nobelprize.org/prizes/economic-sciences/2013/fama/facts/

5. Gerard Rego
5
Heuristics, Less is More – Uncertainty Thinking vs. Complexity
Markowitz Used Heuristics (Not MPT) Markowitz Nobel Prize (MPT)
https://en.wikipedia.org/wiki/Modern_portfolio_theory https://www.nobelprize.org/prizes/economic-sciences/1990/press-release/http://arno.uvt.nl/show.cgi?fid=129399
Markowitz used a simple model 1/n Approach
to Investment, allocating equally to ”n”
options
Harry Markowitz is awarded the Prize Modern
portfolio theory (MPT), or mean-variance analysis,
* Need 500 years of data to prove
the model (assuming all conditions,
stocks and variables exists!
No evidence is found of the outperforming of a
model by another model. This implies that the 1/n
asset allocation strategy, often called the naïve
strategy, is not outperformed by a more sophisticated
models.
Bachelor Thesis Finance
Is the 1/n asset allocation strategy undervalued?
16 October 1990

6. Gerard Rego
6
Heuristics, Better Decision Making
Being applied across industries
Heuristics Decision Making
https://hbr.org/2014/06/instinct-can-beat-analytical-thinking
https://www.youtube.com/watch?v=4VSqfRnxvV8&t=2654s
https://faculty.washington.edu/jmiyamot/p466/pprs/gigerenzer%20heuristic%20decis%20making.pdf
https://en.wikipedia.org/wiki/Uncertainty https://en.wikipedia.org/wiki/Bias%E2%80%93variance_tradeoff

7. Gerard Rego
7
Heuristics are;
i. adaptive tools that ignore
information to make fast
and frugal decisions
ii. that are accurate and robust
under conditions of
uncertainty.
iii. considered ecologically
rational when it functionally
matches the structure of
environment.
https://link.springer.com/content/pdf/10.1007%2Fs41412-017-0058-z.pd
http://www.dangoldstein.com/papers/RecognitionPsychReview.pdf f
Ecological rationality: The recognition
heuristic is ecologically rational if ⍺>0.5
Heuristics 101 – That’s how “People” make decisions

8. Gerard Rego
8
Why Static Models Work on Static Data, Not People
Based on Static Data & Risk Based on Behavior & Uncertainty
101 Examples Analysis Unbundling
Roadmap Data
https://www.accionlabs.com/articles/2018/4/18/is-your-business-prepared-to-use-machine-learning
https://slideplayer.com/slide/14594172/
https://www.amazon.com/Animal-Spirits-Psychology-Economy-Capitalism-ebook-dp-B0037YLBMM/dp/B0037YLBMM/ref=mt_kindle

9. Gerard Rego
9
Era of VUCA (Volatility, Uncertainty, Complexity & Ambiguity)
https://www.forbes.com/sites/jeroenkraaijenbrink/2018/12/19/what-does-vuca-really-mean/#73a9d80717d6
https://blog.irvingwb.com/blog/2018/09/social-physics-making-ai-predictions-easily-accessible.html https://www.ethz.ch/content/dam/ethz/special-interest/gess/chair-of-sociology-dam/documents/icsd2013/0_3_gigerenzer.pdf
• VUCA is a concept that
originated with students at
the U.S. Army War College
after the Cold War.
• And now, the concept is
gaining new relevance to
characterize the current
environment and the
leadership required to
navigate it successfully.
Homo HeuristicusVUCA Accelerating Unbundling

10. Gerard Rego
10
•Tasks that map well-defined
inputs to well-defined outputs, -
e.g., labeling images of specific
animals, the probability of cancer in
medical record, the likelihood of
defaulting on a loan application;
•Large data sets exist or can be
created containing such input-
output pairs, - the bigger the
training data sets the more accurate
the learning;.
When Complex Models Work? When it’s all Static!
But we live in a world of “People’s & Behaviours”
•The capability being learned should be relatively
static, - If the function changes rapidly, retraining is
typically required, including the acquisition of new
training data; and
•No need for detailed explanation of how the
decision was made, - the methods behind a
machine learning recommendation, - subtle
adjustments to the numerical weights that interconnect
its huge number of artificial neurons, - are difficult to
explain because they’re so different from those used by
humans
http://science.sciencemag.org/content/358/6370/1530/tab-pdf
101 Examples Analysis Unbundling
Roadmap Data

11. Gerard Rego
https://searchenterpriseai.techtarget.com/definition/Turing-test https://en.wikipedia.org/wiki/Alan_Turing
Turing to Singularity
Alan Turing Singularity
• Singularity has been reached, Kurzweil says
that machine intelligence will be infinitely
more powerful than all human intelligence
combined
• The Singularity is also the point at which
machines intelligence and humans would
merge.

12. Gerard Rego
12
Evolution, Behaviours & Computing
Darwin, Transmutation of species
CUL-DAR121.- Transcribed by Kees Rookmaaker. (Darwin Online, http://darwin-online.org.uk/
https://due.com/blog/not-strongest-species-survive-charles-darwin/ )
Evolution
• Heritable characteristics over successive
populations
• Expression of genes passed on from
generation to generation
• Different characteristics, rare or
common in a population
• Gives rise to Biodiversity, Variety &
Variability ~ Genetic, Species &
Ecosystem

13. Gerard Rego
Evolutionary Computing - History
Evolutionary Programming - L. Fogel 1962
(San Diego, CA):
Genetic Algorithms - J. Holland 1962 (Ann
Arbor, MI):
Evolution Strategies -I. Rechenberg & H.-P.
Schwefel 1965 (Berlin, Germany):
Genetic Programming - J. Koza 1989 (Palo
Alto, CA):
Recombination
Mutation
Population
Offspring
Parents
Selection
Replacement

14. Gerard Rego
Evolutionary Computing
• Broad Applicability
• Inherently Parallel
• Outperform Classic Methods on Real
Problems
• Evolve with Uncertainty
• Potential to Hybridize
• Capability for Self-Optimization
• Can Solve Problems with no known Solutions
https://pdfs.semanticscholar.org/8c87/d26f409cd56f109b5c0c59f91a5f3e9d632b.pdf https://www.uio.no/studier/emner/matnat/ifi/INF3490/h18/timeplan/slides/lecture3-1pp.pdf
Evolutionary Computing
Evolutionary Computing - Utility

15. Gerard Rego
• Heuristic responses
• Bounded Rationality/Predictably Irrational
• Behaviours of Maximizers/Satisficers
• Ecological Rationality
Intelligence 101 – The Way We See It
http://web.cecs.pdx.edu/~mperkows/CLASS_479/LECTURES479/EVO01.PDF https://hbr.org/2014/06/instinct-can-beat-analytical-thinking
Biological
• Genetic Heuristics
• Ecosystem Diversity
• Feedforward
• Uncertainty
Human

16. Gerard Rego
AI= Heuristic & Ecological Rationality
https://www.fer.unizg.hr/_download/repository/AI-1-Introduction.pdf https://www.ethz.ch/content/dam/ethz/special-interest/gess/chair-of-sociology-dam/documents/icsd2013/0_3_gigerenzer.pdf
To have achieved Human Intelligence is when computing is
able to;
1. Heuristic responses =:True Create and Respond to
Stimuli
2. Bounded Rationality/Predictably Irrational =:True
Rationality
3. Behaviours of Maximizers/Satisficers =: True Satisficing
4. Ecological Rationality =:True Under Certainty &
Uncertainty
When all of the above characteristics have been achieved by
computing we would define this state as being equal to Human
Intelligence or Heuristical Intelligence
Heuristic Intelligence

17. Gerard Rego
Heuristic Computing & Tradeoffs
Tradeoff (Necessary?)
• Optimality – Is the Optimal Solution necessary?
• 40 Trillions of combinations to find the right investing strategy to buy a stock?
https://www.youtube.com/watch?v=a80gPs-ZKp0
• Completeness – Are all solutions necessary?
• UPS - 120 stops – how many permutations & combinations to deliver a package
6,689,502,913,449,135,000, 000, 000,000,000, 000, 000, 000, 000, 000, 000, 000,
000, 000, 000, 000, 000, 000, 000, 000, 000, 000, 000, 000, 000, 000, 000, 000,
000, 000, 000, 000, 000, 000, 000, 000, 000, 000, 000, 000, 000, 000, 000, 000,
000, 000, 000, 000, 000, 000, 000, 000, 000, 000, 000,
000, 000, 000, 000, 000, 000, 000, 000, 000, 000,
https://www.esri.com/~/media/Files/Pdfs/events/ses/2017%20SES/03_SES2017_Jack_Levis.pdf
• Accuracy & Precision – Do we need a high Confidence-Interval for
all situations?
• Where to land the plane with no engines? https://www.wired.com/2009/02/sully-
calmly-to/
• Execution Time – Intractable challenge or NP-Hard problem
• A simple heuristic of Index Investing or complex
MPT/Algorithmic Investing?
https://en.wikipedia.org/wiki/NP-hardness
https://pdfs.semanticscholar.org/8c87/d26f409cd56f109b5c0c59f91a5f3e9d632b.pdf https://www.uio.no/studier/emner/matnat/ifi/INF3490/h18/timeplan/slides/lecture3-1pp.pdf
Heuristic Computing
• Large Search Spaces
• Heuristic Architectures
• Self-learning
• Evolve with Uncertainty
• Can Solve Problems with no known
Solutions

18. Gerard Rego
Evolutionary Intelligence = Ecosystem Heuristics
To have achieved Biological Intelligence is when computing is
able to;
1. Genetic Heuristics =:True Evolve based on ecosystem
stimuli
2. Ecosystem Diversity =:True Create and evolve Genetics
(Increase population diversity – Mutation & Recombination
= Novelty) & Selection (Decrease population diversity ~
Parents & Survivors = Quality)
3. Feedforward =: True Internal and External Ecosystem
Diversity
4. Uncertainty=:True Evolutionary Genetics & Ecosystems
When all of the above has been achieved by computing we would
define this as being equal to Biological Intelligence or
Evolutionary Intelligence

19. Gerard Rego
Human Intelligence & Ecosystem Heuristics
Artificial Intelligence = Heuristic
& Ecological Rationality
Evolutionary Intelligence
= Ecosystem Heuristics
1. Heuristic responses =:True How People Respond to Stimuli
2. Bounded Rationality/Predictably Irrational =:True Rationality
3. Behaviours of Maximizers/Satisficers =: True Satisficing
4. Ecological Rationality =:True Under Uncertainty
1. Genetic Heuristics =:True Evolve based on ecosystem stimuli
2. Ecosystem Diversity =:True Create and evolve Genetics (Increase
population diversity – Mutation & Recombination= Novelty) & Selection
(Decrease population diversity ~ Parents & Survivors = Quality)
3. Feedforward =: True Internal and External Ecosystem Diversity
4. Uncertainty=:True Evolutionary Genetics & Ecosystems

20. Gerard Rego
Singularity
Heuristically Rational & Ecosystem Heuristics
1. Behavioural Heuristic Responses & Genetics Heuristics
2. Behavioural Rationality & Ecosystem Diversity
3. Satisficing Behaviours & Feedforward
4. Ecological Rationality Behaviours & Evolutionary Genetics & Ecosystems

21. Gerard Rego
Heuristic & Evolutionary Computing Applications
Behavioural Economics
Dynamic Optimization
Parallelization
Uncertain Environments
Dynamic Routing Problem
Behavioral BFSI
Dynamic Experiences Behavioural Governance Behavioural Security
Evolutionary Robotics
GOFAI (Good Old Fashioned AI)
Behavioural Health

22. Gerard Rego
Open House for the Heuristically Rational J
All information presented is acknowledged and any data, information, images, etc. not acknowledged by sources is by oversight.