Don't just do something, stand there! Reflections on the counterintuitive behavior of complex systems, seen through the eyes of System Dynamics, Systems Thinking and simulation.
VisualCapitalist has a cool comprehensive map of cognitive biases:
(Click through for the big version.)
The (more or less) corresponding wiki list is here.
It’s a wonder that we ever get anything right!
PsyBlog has some thoughts on antidotes.
h/t Dr MJ
Bongard problems test visual pattern recognition, but there’s no reason to be strict about that. Here’s a slightly nontraditional Bongard problem:
The six on the left conform to a pattern or rule, and your task is to discover it. As an aid, the six boxes on the right do not conform to the same pattern. They might conform to a different pattern, or simply reflect the negation of the rule on the left. It’s possible that more than one rule discriminates between the sets, but the one that I have in mind is not strictly visual (that’s a hint).
If you’re stumped, you might go read this nice article about meta-rationality instead.
I’ll post the solution in a few days. Post your guess in comments (no peeking).
For the 2017 Balaton Group meeting, I’ve updated Sterman & Wittenberg’s Path Dependence, Competition, and Succession in the Dynamics of Scientific Revolution model. The new version is far more usable, with readable variable names and improved diagrams.
This is an extremely interesting model for our current situation of clashing paradigms, fake news and filter bubbles. I encourage you to take a look at the model and paper.
This is actually much more natural as a Ventity model, so watch for another update.
I’m preparing for a talk on the dynamics of dictatorship or authoritarianism, which touches on many other topics, like polarization, conflict, terror and insurgency, and filter bubbles. I thought I’d share a few references, in the hope of attracting more. I’m primarily interested in mathematical models, or at least conceptual models that have clearly-articulated structure->behavior relationships. Continue reading “Dynamics of Dictatorship”
In the near future I’ll be running an experiment with serving advertisements on this site, starting with Google AdSense.
This is motivated by a little bit of greed (to defray the costs of hosting) and a lot of curiosity.
I’m generally wary of running society’s information system on a paid basis. (Recall the first deadly sin of complex system management.) On the other hand, there are certainly valid interests in sharing commercial information.
I plan to write about the outcome down the road, but first I’d like to get some firsthand experience.
What do you think?
Update: The experiment is over.
Here’s a nice example of how AI is killing us now. I won’t dignify this with a link, but I found it posted by a LinkedIn user.
I’d call this an example of artificial stupidity, not AI. The article starts off sounding plausible, but quickly degenerates into complete nonsense that’s either automatically generated or translated, with catastrophic results. But it was good enough to make it past someone’s cognitive filters.
For years, corporations have targeted on World Health Organization to indicate ads to and once to indicate the ads. AI permits marketers to, instead, specialize in what messages to indicate the audience, therefore, brands will produce powerful ads specific to the target market. With programmatic accounting for 67% of all international show ads in 2017, AI is required quite ever to make sure the inflated volume of ads doesn’t have an effect on the standard of ads.
One style of AI that’s showing important promise during this space is tongue process (NLP). informatics could be a psychological feature machine learning technology which will realize trends in behavior and traffic an equivalent method an individual’s brain will. mistreatment informatics during this method can match ads with people supported context, compared to only keywords within the past, thus considerably increasing click rates and conversions.
I was looking at my google stats the other day, curious what posts interest people most. The answer was surprising. Guess what’s #1?
It’s not “Are Causal Loop Diagrams Useful?” (That’s #2.)
It’s not what I’d consider my best technical work, like Bathtub Statistics or Fun with 1D Vector Fields.
It’s not about something controversial, like On Limits to Growth or The alien hail Mary, and other climate policy plays.
Nor is it a hot topic, like Data science meets the bottom line.
It’s not something practical, like Writing an SD Conference Paper.
#1 is the Fibonacci sequence, How Many Pairs of Rabbits Are Created by One Pair in One Year?
Go figure.
My dissertation was a critique and reconstruction of William Nordhaus’ DICE model for climate-economy policy (plus a look at a few other models). I discovered a lot of issues, for example that having a carbon cycle that didn’t conserve carbon led to a low bias in CO2 projections, especially in high-emissions scenarios.
There was one sector I didn’t critique: the climate itself. That’s because Nordhaus used an established model, from climatologists Schneider & Thompson (1981). It turns out that I missed something important: Nordhaus reestimated the parameters of the model from time series temperature and forcing data.
Nordhaus’ estimation focused on a parameter representing the thermal inertia of the atmosphere/surface ocean system. The resulting value was about 3x higher than Schneider & Thompson’s physically-based parameter choice. That delays the effects of GHG emissions by about 15 years. Since the interest rate in the model is about 5%, that lag substantially diminishes the social cost of carbon and the incentive for mitigation.
So … should an economist’s measurement of a property of the climate, from statistical methods, overrule a climatologist’s parameter choice, based on physics and direct observations of structure at other scales?
I think the answer could be yes, IF the statistics are strong and reconcilable with physics or the physics is weak and irreconcilable with observations. So, was that the case?
A nice TED talk explaining how algorithms can reinforce unfairness, inequity and errors of judgment:
Note the discussion of teacher value added modeling. This corresponds with what I found in my own assessment here.
Several countries have now announced eventual bans of internal combustion engines. It’s nice that such a thing can now be contemplated, but this strikes me as a fundamentally flawed approach.
Banning a whole technology class outright is inefficient. When push comes to shove, that inefficiency is likely to lead to an implementation that’s complex and laden with exceptions. Bans and standards are better than nothing, but that regulatory complexity gives opponents something real to whine about. Then the loonies come out. At any plausible corporate cost of capital, a ban in 2040 has near-zero economic weight today.
Rather than banning gas and diesel vehicles at some abstract date in the far future, we should be pricing their externalities now. Air and water pollution, noise, resource extraction, the opportunity cost of space for roads and parking, and a dozen other free rides are good candidates. And, electric vehicles should not be immune to the same charges where applicable.
Once the basic price signal points the transportation market in the right direction, we can see what happens, and tinker around the edges with standards that address particular misperceptions and market failures.