Asides

Just Say No to Complex Equations

Found in an old version of a project model:

IF THEN ELSE( First Time Work Flow[i,Proj,stage
] * TIME STEP >= ( Perceived First Time Scope UEC Work
[i,Proj,uec] + Unstarted Work[i,Proj,stage] )
:OR: Task Is Active[i,Proj,Workstage] = 0
:OR: avg density of OOS work[i,Proj,stage] > OOS density threshold,
Completed work still out of sequence[i,Proj,stage] / TIME STEP
+ new work out of sequence[i,Proj,stage] ,
MIN( Completed work still out of sequence[i,Proj,stage] / Minimum Time to Retrofit Prerequisites into OOS Work
+ new work out of sequence[i,Proj,stage],
new work in sequence[i,Proj,stage]
* ZIDZ( avg density of OOS work[i,Proj,stage],
1 – avg density of OOS work[i,Proj,stage] ) ) )

An equation like this needs to be broken into at least 3 or 4 human-readable chunks. In reviewing papers for the SD conference, I see similar constructions more often than I’d like.

AI for modeling – what (not) to do

Ali Akhavan and Mohammad Jalali have a nice new article in the SDR on the use of AI (LLMs) to complement simulation modeling.

Generative AI and simulation modeling: how should you (not) use large language models like ChatGPT

Ali Akhavan, Mohammad S. Jalali

Abstract

Generative Artificial Intelligence (AI) tools, such as Large Language Models (LLMs) and chatbots like ChatGPT, hold promise for advancing simulation modeling. Despite their growing prominence and associated debates, there remains a gap in comprehending the potential of generative AI in this field and a lack of guidelines for its effective deployment. This article endeavors to bridge these gaps. We discuss the applications of ChatGPT through an example of modeling COVID-19’s impact on economic growth in the United States. However, our guidelines are generic and can be applied to a broader range of generative AI tools. Our work presents a systematic approach for integrating generative AI across the simulation research continuum, from problem articulation to insight derivation and documentation, independent of the specific simulation modeling method. We emphasize while these tools offer enhancements in refining ideas and expediting processes, they should complement rather than replace critical thinking inherent to research.

It’s loaded with useful examples of prompts and responses:

I haven’t really digested this yet, but I’m looking forward to writing about it. In the meantime, I’m very interested to hear your take in the comments.

Defining SD

Open Access Note by Asmeret Naugle, Saeed Langarudi, Timothy Clancy: https://doi.org/10.1002/sdr.1762

Abstract
A clear definition of system dynamics modeling can provide shared understanding and clarify the impact of the field. We introduce a set of characteristics that define quantitative system dynamics, selected to capture core philosophy, describe theoretical and practical principles, and apply to historical work but be flexible enough to remain relevant as the field progresses. The defining characteristics are: (1) models are based on causal feedback structure, (2) accumulations and delays are foundational, (3) models are equation-based, (4) concept of time is continuous, and (5) analysis focuses on feedback dynamics. We discuss the implications of these principles and use them to identify research opportunities in which the system dynamics field can advance. These research opportunities include causality, disaggregation, data science and AI, and contributing to scientific advancement. Progress in these areas has the potential to improve both the science and practice of system dynamics.

I shared some earlier thoughts here, but my refined view is in the SDR now:


Invited Commentaries by Tom Fiddaman, Josephine Kaviti Musango, Markus Schwaninger, Miriam Spano: https://doi.org/10.1002/sdr.1763

Thyroid Dynamics: Chartjunk

I just ran across a funny instance of TSH nonlinearity. Check out the axis on this chart:

It’s actually not as bad as you’d think: the irregular axis is actually a decent approximation of a log-linear scale:

My main gripe is that the perceptual midpoint of the ATA range bar on the chart is roughly 0.9, whereas the true logarithmic midpoint is more like 1.6. The NACB bar is similarly distorted.

Spreadsheets Strike Again

In this BBC podcast, stand-up mathematician Matt Parker explains the latest big spreadsheet screwup: overstating European productivity growth.

There are a bunch of killers in spreadsheets, but in this case the culprit was lack of a time axis concept, making it easy to misalign times for the GDP and labor variables. The interesting thing is that a spreadsheet’s strong suite – visibility of the numbers – didn’t help. Someone should have seen 22% productivity growth and thought, “that’s bonkers” – but perhaps expectations of a COVID19 rebound short-circuited the mental reality check.

Believing Exponential Growth

Verghese: You were prescient about the shape of the BA.5 variant and how that might look a couple of months before we saw it. What does your crystal ball show of what we can expect in the United Kingdom and the United States in terms of variants that have not yet emerged?

Pagel: The other thing that strikes me is that people still haven’t understood exponential growth 2.5 years in. With the BA.5 or BA.3 before it, or the first Omicron before that, people say, oh, how did you know? Well, it was doubling every week, and I projected forward. Then in 8 weeks, it’s dominant.

It’s not that hard. It’s just that people don’t believe it. Somehow people think, oh, well, it can’t happen. But what exactly is going to stop it? You have to have a mechanism to stop exponential growth at the moment when enough people have immunity. The moment doesn’t last very long, and then you get these repeated waves.

You have to have a mechanism that will stop it evolving, and I don’t see that. We’re not doing anything different to what we were doing a year ago or 6 months ago. So yes, it’s still evolving. There are still new variants shooting up all the time.

At the moment, none of these look devastating; we probably have at least 6 weeks’ breathing space. But another variant will come because I can’t see that we’re doing anything to stop it.

– Medscape, We Are Failing to Use What We’ve Learned About COVID, Eric J. Topol, MD; Abraham Verghese, MD; Christina Pagel, PhD

There are no decision makers…

A little gem from Jay Forrester:

One hears repeatedly the question of how we in system dynamics might reach “decision makers.” With respect to the important questions, there are no decision makers. Those at the top of a hierarchy only appear to have influence. They can act on small questions and small deviations from current practice, but they are subservient to the constituencies that support them. This is true in both government and in corporations. The big issues cannot be dealt with in the realm of small decisions. If you want to nudge a small change in government, you can apply systems thinking logic, or draw a few causal loop diagrams, or hire a lobbyist, or bribe the right people. However, solutions to the most important sources of social discontent require reversing cherished policies that are causing the trouble. There are no decision makers with the power and courage to reverse ingrained policies that would be directly contrary to public expectations. Before one can hope to influence government, one must build the public constituency to support policy reversals.

Nordhaus on Subsidies

I’m not really a member of the neoclassical economics fan club, but I think this is on point:

“Subsidies pose a more general problem in this context. They attempt to discourage carbon-intensive activities by making other activities more attractive. One difficulty with subsidies is identifying the eligible low-carbon activities. Why subsidize hybrid cars (which we do) and not biking (which we do not)? Is the answer to subsidize all low carbon activities? Of course, that is impossible because there are just too many low-carbon activities, and it would prove astronomically expensive. Another problem is that subsidies are so uneven in their impact. A recent study by the National Academy of Sciences looked at the impact of several subsidies on GHG emissions. It found a vast difference in their effectiveness in terms of CO2removed per dollar of subsidy. None of the subsidies were efficient; some were horribly inefficient; and others such as the ethanol subsidy were perverse and actually increased GHG emissions. The net effect of all the subsidies taken together was effectively zero!” So in the end, it is much more effective to penalize carbon emissions than to subsidize everything else.” (Nordhaus, 2013, p. 266)

(Via a W. Hogan paper, https://scholar.harvard.edu/whogan/files/hogan_hepg_100418r.pdf)