Climate Catastrophe Loops

PNAS has a new article on climate catastrophe mechanisms, focused on the social side, not natural tipping points. The article includes a causal loop diagram capturing some of the key feedbacks:

The diagram makes an unconventional choice: link polarity is denoted by dashed lines, rather than the usual + and – designations at arrowheads. Per the caption,

This is a causal loop diagram, in which a complete line represents a positive polarity (e.g., amplifying feedback; not necessarily positive in a normative sense) and a dotted line denotes a negative polarity (meaning a dampening feedback).

Does this new convention work? I don’t think so. It’s not less visually cluttered, and it makes negative links look tentative, though in fact there’s no reason for a negative link to have any less influence than a positive one. I think it makes it harder to assess loop polarity by following reversals from – links. There’s at least one goof: increasing ecosystem services should decrease food and water shortages, so that link should have negative polarity.

The caption also confuses link and loop polarity: “a complete line represents a positive polarity (e.g., amplifying feedback”. A single line is a causal link, not a loop, and therefore doesn’t represent feedback at all. (The rare exception might be a variable with a link back to itself, sometimes used to indicate self-reinforcement without elaborating on the mechanism.)

Nevertheless, I think this is a useful start toward a map of the territory. For me, it was generative, i.e. it immediately suggested a lot of related effects. I’ve elaborated on the original here:

  1. Food, fuel and water shortages increase pressure to consume more natural resources (biofuels, ag land, fishing for example) and therefore degrade biodiversity and ecosystem services. (These are negative links, but I’m not following the dash convention – I’m leaving polarity unlabeled for simplicity.) This is perverse, because it creates reinforcing loops worsening the resource situation.
  2. State fragility weakens protections that would otherwise protect natural resources against degradation.
  3. Fear of scarcity induces the wealthy to protect their remaining resources through rent seeking, corruption and monopoly.
  4. Corruption increases state fragility, and fragile states are less able to defend against further corruption.
  5. More rent seeking, corruption and monopoly increases economic inequality.
  6. Inequality, rent seeking, corruption, and scarcity all make emissions mitigation harder, eventually worsening warming.
  7. Displacement breeds conflict, and conflict displaces people.
  8. State fragility breeds conflict, as demagogues blame “the other” for problems and nonviolent conflict resolution methods are less available.
  9. Economic inequality increases mortality, because mortality is an extreme outcome, and inequality puts more people in the vulnerable tail of the distribution.

#6 is key, because it makes it clear that warming is endogenous. Without it, the other variables represent a climate-induced cascade of effects. In reality, I think we’re already seeing many of the tipping effects (resource and corruption effects on state fragility, for example) and the resulting governance problems are a primary cause of the failure to reduce emissions.

I’m sure I’ve missed a bunch of links, but this is already a case of John Muir‘s idea, “When we try to pick out anything by itself, we find it hitched to everything else in the Universe.”

Unfortunately, most of the hitches here create reinforcing loops, which can amplify our predicament and cause catastrophic tipping events. I prefer to see this as an opportunity: we can run these vicious cycles in reverse, making them virtuous. Fighting corruption makes states less fragile, making mitigation more successful, reducing future warming and the cascade of side effects that would otherwise reinforce state fragility in the future. Corruption is just one of many places to start, and any progress is amplified. It’s just up to us to cross enough virtuous tipping points to get the whole system moving in a good direction.

Grand Challenges for Socioeconomic Systems Modeling

Following my big tent query, I was reexamining Axtell’s critique of SD aggregation and my response. My opinion hasn’t changed much: I still think Axtell’s critique of aggregation is very useful, albeit directed at a straw dog vision of SD that doesn’t exist, and that building bridges remains important.

As I was attempting to relocate the critique document, I ran across this nice article on Eight grand challenges in socio-environmental systems modeling.

Modeling is essential to characterize and explore complex societal and environmental issues in systematic and collaborative ways. Socio-environmental systems (SES) modeling integrates knowledge and perspectives into conceptual and computational tools that explicitly recognize how human decisions affect the environment. Depending on the modeling purpose, many SES modelers also realize that involvement of stakeholders and experts is fundamental to support social learning and decision-making processes for achieving improved environmental and social outcomes. The contribution of this paper lies in identifying and formulating grand challenges that need to be overcome to accelerate the development and adaptation of SES modeling. Eight challenges are delineated: bridging epistemologies across disciplines; multi-dimensional uncertainty assessment and management; scales and scaling issues; combining qualitative and quantitative methods and data; furthering the adoption and impacts of SES modeling on policy; capturing structural changes; representing human dimensions in SES; and leveraging new data types and sources. These challenges limit our ability to effectively use SES modeling to provide the knowledge and information essential for supporting decision making. Whereas some of these challenges are not unique to SES modeling and may be pervasive in other scientific fields, they still act as barriers as well as research opportunities for the SES modeling community. For each challenge, we outline basic steps that can be taken to surmount the underpinning barriers. Thus, the paper identifies priority research areas in SES modeling, chiefly related to progressing modeling products, processes and practices.

Elsawah et al., 2020

The findings are nicely summarized in Figure 1:


Click to Enlarge

Not surprisingly, item #1 is … building bridges. This is why I’m more of a “big tent” guy. Is systems thinking a subset of system dynamics, or is system dynamics a subset of systems thinking? I think the appropriate answer is, “who cares?” Such disciplinary fence-building is occasionally informative, but more often needlessly divisive and useless for solving real-world problems.

It’s interesting to contrast this with George Richardson’s list for SD:

The potential pitfalls of our current successes suggest the time is right to sketch a view of outstanding problems in the field of system dynamics, to focus the attention of people in the field on especially promising or especially problematic issues. …

Understanding model behavior
Accumulating wise practice
Advancing practice
Accumulating results
Making models accessible
Qualitative mapping and formal modeling
Widening the base
Confidence and validation

Problems for the Future of System Dynamics
George P. Richardson

The contrasts here are interesting. Elsewah et al. are more interested in multiscale phenomena, data, uncertainty and systemic change (#5, which I think means autopoeisis, not merely change over time). I think these are all important and perhaps underappreciated priorities for the future of SD as well. Richardson on the other hand is more interested in validation and understanding of models, making progress cumulative, and widening participation in several ways.

More importantly, I think there’s really a lot of overlap – in fact I don’t think either party would disagree with anything on the other’s list. In particular, both support mixed qualitative and computational methods and increasing the influence of models.

I think Forrester’s view on influence is illuminating:

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.

System Dynamics—the Next Fifty Years
Jay W. Forrester

This neatly explains Forrester’s emphasis on education as a prerequisite for change. Richardson may agree, because this is essentially “widening the base” and “making models accessible”. My first impression was that Elsawah et al. were taking more of a “modeling priesthood” view of things, but in the end they write:

New kinds of interactive interfaces are also needed to help stakeholders access models, be it to make sense of simulation results (e.g. through monetization of values or other forms of impact representation), to shape assumptions and inputs in model development and scenario building, and to actively negotiate around inevitable conflicts and tradeoffs. The role of stakeholders should be much more expansive than a passive from experts, and rather is a co-creator of models, knowledge and solutions.

Where I sit in post-covid America, with atavistic desires for simpler times that never existed looming large in politics, broadening the base for model participation seems more important than ever. It’s just a bit daunting to compare the long time constant on learning with the short fuse on some of the big problems we hope these grand challenges will solve.

Should System Dynamics Have a Big Tent or Narrow Focus?

In a breakout in the student colloquium at ISDC 2022, we discussed the difficulty of getting a paper accepted into the conference, where the content was substantially a discrete event or agent simulation. Readers may know that I’m not automatically a fan of discrete models. Discrete time stinks. However, I think “discreteness” itself is not the enemy – it’s just that the way people approach some discrete models is bad, and continuous is often a good way to start.

On the flip side, there are certainly cases in which it’s sensible to start with a more granular, detailed model. In fact there are cases in which nonlinearity makes correct aggregation impossible in principle. This may not require going all the way to a discrete, agent model, but I think there’s a compelling case for the existence of systems in which the only good model is not a classic continuous time, aggregate, continuous value model. In between, there are also cases in which it may be practical to aggregate, but you don’t know how to do it a priori. In such cases, it’s useful to compare aggregate models with underlying detailed models to see what the aggregation rules should be, and to know where they break down.

I guess this is a long way of saying that I favor a “big tent” interpretation of System Dynamics. We should be considering models broadly, with the goal of understanding complex systems irrespective of methodological limits. We should go where operational thinking takes us, even if it’s not continuous.

This doesn’t mean that everything is System Dynamics. I think there are lots of things that should generally be excluded. In particular, anything that lacks dynamics – at a minimum pure stock accumulation, but usually also feedback – doesn’t make the cut. While I think that good SD is almost always at the intersection of behavior and physics, we sometimes have nonbehavioral models at the conference, i.e. models that lack humans, and that’s OK because there are some interesting opportunities for cross-fertilization. But I would exclude models that address human phenomena, but with the kind of delusional behavioral model that you get when you assume perfect information, as in much of economics.

I think a more difficult question is, where should we draw the line between System Dynamics and model-free Systems Thinking? I think we do want some model-free work, because it’s the gateway drug, and often influential. But it’s also high risk, in the sense that it may involve drawing conclusions about behavior from complex maps, where we’ve known from the beginning that no one can intuitively solve a 10th order system. I think preserving the core of the SD genome, that conclusions should emerge from replicable, transparent, realistic simulations, is absolutely essential.

Related:

Discrete Time Stinks

Dynamics of the last Twinkie

Bernoulli and Poisson are in a bar …

Modeling Discrete & Stochastic Events in Vensim

Finding SD conference papers

There’s been a lot of turbulence in the SD society web organization, which is greatly improved. One side effect is that conference proceedings have moved. The conference proceedings page now points to a dedicated subdomain.

If you want to do a dedicated search of the proceedings for papers on a particular topic, the google search syntax is now:

site:proceedings.systemdynamics.org topic

where ‘topic’ should be replaced by your terms of interest, as in

site:proceedings.systemdynamics.org stock flow

(This post was originally published in Oct. 2012; obsolete approaches have been removed for simplicity.)

Nature Reverses on Limits

Last week Nature editorialized,

Are there limits to economic growth? It’s time to call time on a 50-year argument

Fifty years ago this month, the System Dynamics group at the Massachusetts Institute of Technology in Cambridge had a stark message for the world: continued economic and population growth would deplete Earth’s resources and lead to global economic collapse by 2070. This finding was from their 200-page book The Limits to Growth, one of the first modelling studies to forecast the environmental and social impacts of industrialization.

For its time, this was a shocking forecast, and it did not go down well. Nature called the study “another whiff of doomsday” (see Nature 236, 47–49; 1972). It was near-heresy, even in research circles, to suggest that some of the foundations of industrial civilization — mining coal, making steel, drilling for oil and spraying crops with fertilizers — might cause lasting damage. Research leaders accepted that industry pollutes air and water, but considered such damage reversible. Those trained in a pre-computing age were also sceptical of modelling, and advocated that technology would come to the planet’s rescue. Zoologist Solly Zuckerman, a former chief scientific adviser to the UK government, said: “Whatever computers may say about the future, there is nothing in the past which gives any credence whatever to the view that human ingenuity cannot in time circumvent material human difficulties.”

“Another Whiff of Doomsday” (unpaywalled: Nature whiff of doomsday 236047a0.pdf) was likely penned by Nature editor John Maddox, who wrote in his 1972 book, the Doomsday Syndrome,

“Tiny though the earth may appear from the moon, it is in reality an enormous object. The atmosphere of the earth alone weighs more than 5,000 million million tons, more than a million tons of air for each human being now alive. The water on the surface of the earth weights more than 300 times as much – in other words, each living person’s share of the water would just about fill a cube half a mile in each direction… It is not entirely out of the question that human intervention could at some stage bring changes, but for the time being the vast scale on which the earth is built should be a great comfort. In other words, the analogy of space-ship earth is probably not yet applicable to the real world. Human activity, spectacular though it may be, is still dwarfed by the human environment.”

Reciting the scale of earth’s resources hasn’t held up well as a counterargument to Limits., for the reason given by Forrester and Meadows et al. at the time: exponential growth approaches any finite limit in a relatively small number of doublings. The Nature editors were clearly aware of this back in ’72, but ignored its implications:

Instead, they subscribed to a “smooth approach” view, in which “a kind of restraint” limits population all by itself:

There are a lot of problems with this reasoning, not least of which is that economic activity is growing faster than population, yet there is no historic analog of the demographic transition for economies. However, I think the most fundamental problem with the editors’ mental model is that it’s effectively first order. Population is the only stock of interest; to the extent that they mention resources and pollution, it is only to propose that prices and preferences will take care of them. There’s no consideration of the possibility of a laissez-faire demographic transition resulting in absolute levels of population and economic activity requiring resource withdrawals that deplete resources and saturate sinks, leading to eventual overshoot and collapse. I’m reminded of Jay Forrester’s frequent comment, to the effect of, “if you have a model, you’ll be the only person in the room who can speak for 20 minutes without self-contradiction.” The ’72 Nature editorial clearly suffers for lack of a model.

While the ’22 editorial at last acknowledges the existence of the problem, its prescription is “more research.”

Researchers must try to resolve a dispute on the best way to use and care for Earth’s resources.

But the debates haven’t stopped. Although there’s now a consensus that human activities have irreversible environmental effects, researchers disagree on the solutions — especially if that involves curbing economic growth. That disagreement is impeding action. It’s time for researchers to end their debate. The world needs them to focus on the greater goals of stopping catastrophic environmental destruction and improving well-being.

… green-growth and post-growth scientists need to see the bigger picture. Right now, both are articulating different visions to policymakers, and there is a risk this will delay action. In 1972, there was still time to debate, and less urgency to act. Now, the world is running out of time.

If there’s disagreement about the solution, then the solution should be distributed, so that we can learn from different approaches. It’s easy to verify success, by checking the equilibrium conditions for sources and sinks: as long as they’re in decline, policies need to adjust. However, I don’t think lack of agreement about the solution is the real problem.

The real problem is that the research “consensus that human activities have irreversible environmental effects” has no counterpart in the political and economic spheres. Neither green-growth nor degrowth has de facto support. This is not a problem that will be solved by more environmental or economic research.

Escalator Solutions

As promised, here’s my solution to the escalator problem … several, actually.

Before getting into the models, a point about simulation vs. analytic solutions. You can solve this problem on pencil and paper with simple algebra. This has some advantages. First, you can be completely data free, by using symbols exclusively. You don’t need to know the height of the stair or a person’s climbing speed, because you can call these Hs and Vc and solve the problem for all possible values. A simulation, by contrast, needs at least notional values for these things. Second, you may be able to draw general conclusions about the solution from its structure. For example, if it takes the form t = H/V, you know there’s some kind of singularity at V=0. With a simulation, if you don’t think to test V=0, you might miss an important special case. It’s easy to miss these special cases in a parameter space with many dimensions.

On the other hand, if there are many dimensions, this may imply that the problem will be difficult or impossible to solve analytically, so simulation may be the only fallback. A simulation also makes it easier to play with the model interactively (e.g., Vensim’s Synthesim mode) and to incorporate features like model-data comparisons and optimization. The ability to play invites experimentation with parameter values you might not otherwise think of. Also, drawing a stock-flow diagram may allow you to access other forms of visual thinking, or analogies with structurally similar systems in different domains.

With that prelude, here’s how I conceived of the problem:

  • You’re in a building, at height=0 (feet in my model, but the particular unit doesn’t matter as long as you have and check units).
  • Stairs rise to height=100.
  • There’s an escalator from 100 to 200 ft.
  • Then stairs resume, to infinite height.
  • The escalator ascends at 1ft/sec and the climber at 1ft/sec whether on stairs or not.
  • At some point, the climber rests for 60sec, at which point their rate of climb is 0, but they continue to ascend if on the escalator.

Of course all the numbers can be changed on the fly, but these concepts at least have to exist.

I think of this as a problem of pure accumulation, with height as a stock. But it turned out that I still needed some feedback to determine where the climber was – on the stairs, or on the escalator:

At first it struck me that this was “fake” feedback – an accounting artifact – and that it might go away with an alternate conception. Here’s my implementation of Pradeesh Kumar’s idea, from the SDS Discussion Group on Facebook, with the height to be climbed on the stairs and escalator as a stock, with an outflow as climbing is accomplished:The logical loop is still there, and the rest of the accounting is more complex, so I think it’s inevitable.

Finally, I built the same model in Ventity, so I could use multiple entities to quickly store and replicate several scenarios:

Looking at the Ventity output, resting on the escalator is preferable:

While resting on the stairs, nothing happens. While resting on the escalator, you continue to make gains.

There’s an unstated assumption present in all the twitter answers I’ve seen: the escalator is the up escalator. I actually prefer to go up the down escalator, though it attracts weird looks. If you do that, resting on the escalator is catastrophic, because you lose ground that you previously gained:

I suspect there are other interesting edge cases to explore.

The models:

Vensim (any version): Escalator 1.mdl

Vensim, alternate conception: Escalator 1 alt.mdl

Vensim Pro/DSS/Model Reader – subscripted for multiple experiments: escalator 2.mdl

Ventity: Escalator 1.zip

JJ Lauble has also created a version, posted at the Vensim forum. I haven’t had a chance to explore it yet, but it looks like he may have used Vensim to explore the algebraic solution, with the time axis as a way to scan the solution space with Synthesim overrides.

Modeling Chronic Wasting Disease

I’ve been too busy to post much lately, because I’ve been busy with projects in city energy planning, web interfaces, and chronic wasting disease (CWD) in deer, plus a lot of Vensim and Ventity testing.

I’m hoping to write a little more about CWD, because it’s very interesting (and very nasty). We’ve been very successful at blending Structured Decision Making (SDM) with SD modeling in Wisconsin’s 10-yr plan review. We’ve been able to use models live in a rather diverse stakeholder group, including non-modelers. The model has worked well as a shared thinking tool, triggering some really good discussions, without getting mired in black-box problems.

The video below is from an “under the hood” session that looked into the details of the model for an interested subset of participants, so it’s probably nerdier than other more policy-oriented discussions, but also of greater interest to modelers I hope.

I’ll have more to say about SD in CWD policy and the marriage of SD and SDM soon, I hope.

Escalator Problems

@stevenstrogatz reposts a clever, simple problem:

two people climb a staircase and then climb an escalator. One person rests a minute on the staircase and the other rests a minute on the escalator, but otherwise they climb stairs at the same rate. Who is faster or are they equally fast?

There’s also an airport-walkway version that adds special relativity as a twist.

It’s interesting to see the varied thought processes in the comments. Pencil and paper is often quicker and yields useful analytic insight, but these are both accumulation problems, and therefore good candidates for SD simulation.

How would you model this situation? (I’ll post my answer in a day or two.)

Mask Mandates and One Study Syndrome

The evidence base for Montana’s new order promoting parental opt-out from school mask mandates relies heavily on two extremely weak studies.

Montana Governor Gianforte just publicized a new DPHHS order requiring schools to provide a parental opt-out for mask requirements.

Underscoring the detrimental impact that universal masking may have on children, the rule cites a body of scientific literature that shows side effects and dangers from prolonged mask wearing.

The order purports to be evidence based. But is the evidence any good?

Mask Efficacy

The order cites:

The scientific literature is not conclusive on the extent of the impact of
masking on reducing the spread of viral infections. The department understands
that randomized control trials have not clearly demonstrated mask efficacy against
respiratory viruses, and observational studies are inconclusive on whether mask use
predicts lower infection rates, especially with respect to children.
1

The supporting footnote is basically a dog’s breakfast,

1 See, e.g., Guerra, D. and Guerra, D., Mask mandate and use efficacy for COVID-19 containment in
US States, MedRX, Aug. 7, 2021, https://www.medrxiv.org/content/10.1101/2021.05.18.21257385v2
(“Randomized control trials have not clearly demonstrated mask efficacy against respiratory viruses,
and observational studies conflict on whether mask use predicts lower infection rates.”). Compare
CDC, Science Brief: Community Use of Cloth Masks to Control the Spread of SARS-CoV-2, last
updated May 7, 2021, https://www.cdc.gov/coronavirus/2019-ncov/science/science-briefs/masking-
science-sars-cov2.html, last visited Aug. 30, 2021 (mask wearing reduces new infections, citing
studies)
….

(more stuff of declining quality)

This is not an encouraging start; it’s blatant cherry picking. Guerra & Guerra is an observational statistical test of mask mandates. The statement DPHHS quotes, “Randomized control trials have not clearly demonstrated mask efficacy…” isn’t even part of the study; it’s merely an introductory remark in the abstract.

Much worse, G&G isn’t a “real” model. It’s just a cheap regression of growth rates against mask mandates, with almost no other controls. Specifically, it omits NPIs, weather, prior history of the epidemic in each state, and basically every other interesting covariate, except population density. It’s not even worth critiquing the bathtub statistics issues.

G&G finds no effects of mask mandates. But is that the whole story? No. Among the many covariates they omit is mask compliance. It turns out that matters, as you’d expect. From Leech et al. (one of many better studies DPHHS ignored):

Across these analyses, we find that an entire population wearing masks in public leads to a median reduction in the reproduction number R of 25.8%, with 95% of the medians between 22.2% and 30.9%. In our window of analysis, the median reduction in R associated with the wearing level observed in each region was 20.4% [2.0%, 23.3%]1. We do not find evidence that mandating mask-wearing reduces transmission. Our results suggest that mask-wearing is strongly affected by factors other than mandates.

We establish the effectiveness of mass mask-wearing, and highlight that wearing data, not mandate data, are necessary to infer this effect.

Meanwhile, the DPHHS downplays its second citation, the CDC Science Brief, which cites 65 separate papers, including a number of observational studies that are better than G&G. It concludes that masks work, by a variety of lines of evidence, including mechanistic studies, CFD simulations and laboratory experiments.

Verdict: Relying on a single underpowered, poorly designed regression to make sweeping conclusions about masks is poor practice. In effect, DPHHS has chosen the one earwax-flavored jellybean from a bag of more attractive choices.

Mask Safety

The department order goes on,

The department
understands, however, that there is a body of literature, scientific as well as
survey/anecdotal, on the negative health consequences that some individuals,
especially some children, experience as a result of prolonged mask wearing.
2

The footnote refers to Kisielinski et al. – again, a single study in a sea of evidence. At least this time it’s a meta-analysis. But was it done right? I decided to spot check.

K et al. tabulate a variety of claims conveniently in Fig. 2:

The first claim assessed is that masks reduce O2, so I followed those citations.

Citation Claim Assessment/Notes
Beder 2008 Effect Effect, but you can’t draw any causal conclusion because there’s no control group.
Butz 2005 No effect PhD Thesis, not available for review
Epstein 2020 No effect No effect (during exercise)
Fikenzer 2020 Effect Effect
Georgi 2020 Effect Gray literature, not available for review
Goh 2019 No effect No effect; RCT n~=100 children
Jagim 2018 Effect Not relevant – this concerns a mask designed for elevation training, i.e. deliberately impeding O2
Kao 2004 Effect Effect. End stage renal patients.
Kyung 2020 Effect Dead link. Flaky journal? COPD patients.
Liu 2020 Effect Small effect – <1% SpO2. Nonmedical conference paper, so dubious peer review. N=12.
Mo 2020 No effect No effect. Gray lit. COPD patients.
Person 2018 No effect No effect. 6 minute walking test.
Pifarre 2020 Effect Small effect. Tiny sample (n=8). Questionable control of order of test conditions. Exercise.
Porcari 2016 Effect Irrelevant – like Jagim, concerns an elevation training mask.
Rebmann 2013 Effect No effect. “There were no changes in nurses’ blood pressure, O2 levels, perceived comfort, perceived thermal comfort, or complaints of visual difficulties compared with baseline levels.” Also, no control, as in Beder.
Roberge 2012 No effect No effect. N=20.
Roberge 2014 No effect No effect. N=22. Pregnancy.
Tong 2015 Effect Effect. Exercise during regnancy.

If there’s a pattern here, it’s lots of underpowered small sample studies with design defects. Morover, there are some blatant errors in assessment of relevance (Jagim, Porcari) and inclusion of uncontrolled studies (Beder, Rebmann, maybe Pifarre). In other words, this is 30% rubbish, and the rubbish is all on the “effect” side of the scale.

If the authors did a poor job assessing the studies they included, I also have to wonder whether they did a bad screening job. That turns out to be hard to determine without more time. But a quick search does reveal that there has been an explosion of interest in the topic, with a number of new studies in high-quality journals with better control designs. Regrettably, sample sizes still tend to be small, but the results are generally not kind to the assertions in the health order:

Mapelli et al. 2021:

Conclusions Protection masks are associated with significant but modest worsening of spirometry and cardiorespiratory parameters at rest and peak exercise. The effect is driven by a ventilation reduction due to an increased airflow resistance. However, since exercise ventilatory limitation is far from being reached, their use is safe even during maximal exercise, with a slight reduction in performance.

Chan, Li & Hirsch 2020:

In this small crossover study, wearing a 3-layer nonmedical face mask was not associated with a decline in oxygen saturation in older participants. Limitations included the exclusion of patients who were unable to wear a mask for medical reasons, investigation of 1 type of mask only, Spo2 measurements during minimal physical activity, and a small sample size. These results do not support claims that wearing nonmedical face masks in community settings is unsafe.

Lubrano et al. 2021:

This cohort study among infants and young children in Italy found that the use of facial masks was not associated with significant changes in Sao2 or Petco2, including among children aged 24 months and younger.

Shein et al. 2021:

The risk of pathologic gas exchange impairment with cloth masks and surgical masks is near-zero in the general adult population.

A quick trip to PubMed or Google Scholar provides many more.

Verdict: a sloppy meta-analysis is garbage-in, garbage-out.

Bottom Line

Montana DPHHS has failed to verify its sources, ignores recent literature and therefore relies on far less than the best available science in the construction of its flawed order. Its sloppy work will fan the flames of culture-war conspiracies and endanger the health of Montanans.

I CAN HAS SYSTEM DYNAMICZ?

IM PRETTY SURE THIS IS THE FURST EVAH SYSTEM DYNAMICZ SIMULASHUN MODEL WRITTEN IN LOLCODE.

HAI 1.2
    VISIBLE "HAI, JWF!"
    
    OBTW
     ==========================================================================
     SYSTEM DYNAMICZ INVENTORY MODEL IN LOLCODE
     TOM FIDDAMAN, METASD.COM, 2021
     INSPIRED BY THE CLASSIC BEER GAME
     AND MODEL 3.10 OF MICHAEL GOODMAN'S 
     'STUDY NOTES IN SYSTEM DYNAMICS'
     ==========================================================================
    TLDR
    
    BTW FUNKTION 4 INTEGRATIN STOCKZ WITH NET FLOW INOUT
    HOW IZ I INTEGRATIN YR STOCK AN YR INOUT AN YR TIMESTEP
        FOUND YR SUM OF STOCK AN PRODUKT OF INOUT AN TIMESTEP
    IF U SAY SO
    
    BTW FUNKTION 4 CHARACTER PLOTZ
    HOW IZ I PLOTTIN YR X AN YR SYMBOL
        I HAS A STRING ITZ ""
        I HAS A COUNT ITZ 0
        IM IN YR XLOOP
            BOTH SAEM COUNT AN BIGGR OF COUNT AN X, O RLY?
                YA RLY, GTFO
                NO WAI, STRING R SMOOSH " " STRING MKAY
            OIC
            COUNT R SUM OF COUNT AN 1
        IM OUTTA YR XLOOP
        VISIBLE SMOOSH STRING SYMBOL MKAY
    IF U SAY SO
    
    BTW INISHUL TIME - DEKLARE SUM VARIABLZ AND INIT STOCKZ

    I HAS A INV ITZ 0.0         BTW INVENTORY (WIDGETS)
    I HAS A MAKIN               BTW PRODUCTION RATE (WIDGETS/WEEK)
    I HAS A SELLIN              BTW SALES RATE (WIDGETS/WEEK)
    I HAS A TIME ITZ 0.0        BTW LOL I WISH (WEEK)
    I HAS A TIMESTEP ITZ 1.0    BTW SIMULATION TIME STEP (WEEK)
    I HAS A ZEND ITZ 50.0       BTW FINAL TIME OF THE SIM (WEEK)
    I HAS A TARGET ITZ 20.0     BTW DESIRED INVENTORY (WIDGETS)
    I HAS A ADJTIME ITZ 4.0     BTW INVENTORY ADJUSTMENT TIME (WEEK)
    I HAS A ORDERIN             BTW ORDER RATE (WIDGETS/WEEK)
    I HAS A INIORDERS ITZ 10.0  BTW INITIAL ORDER RATE (WIDGETS/WEEK)
    I HAS A STEPTIME ITZ 30.0   BTW TIME OF STEP IN ORDERS (WEEK)
    I HAS A STEPSIZE ITZ 5.0    BTW SIZE OF STEP IN ORDERS (WIDGETS/WEEK)
    I HAS A INVADJ              BTW INVENTORY ADJUSTMENT NEEDED (WIDGETS)
    I HAS A WIP ITZ 0.0         BTW WORK IN PROGRESS INVENTORY (WIDGETS)
    I HAS A SHIPPIN             BTW DELIVERIES FROM WIP (WIDGETS/WEEK)
    I HAS A PRODTIME ITZ 4.0    BTW TIME TO PRODUCE (WEEK)
    
    VISIBLE "SHOWIN RESULTZ FOR PRODUKSHUN"
    
    IM IN YR SIMLOOP        BTW MAIN SIMULASHUN LOOP
        
        BTW CALCULATE RATES AND AUXILIARIES
        
        BTW STEP IN CUSTOMER ORDERS
        BOTH SAEM TIME AN BIGGR OF TIME AN STEPTIME, O RLY?
            YA RLY, ORDERIN R SUM OF INIORDERS AN STEPSIZE
            NO WAI, ORDERIN R INIORDERS
        OIC
        
        SELLIN R SMALLR OF ORDERIN AN QUOSHUNT OF INV AN TIMESTEP
        INVADJ R DIFF OF TARGET AN INV
        MAKIN R SUM OF SELLIN AN QUOSHUNT OF INVADJ AN ADJTIME
        MAKIN R BIGGR OF MAKIN AN 0.0
        SHIPPIN R QUOSHUNT OF WIP AN PRODTIME
        
        BTW PLOT
        VISIBLE SMOOSH TIME " " MAKIN MKAY
        BTW PRODUKT WITH SCALE FACTOR FOR SIZING
        I IZ PLOTTIN YR PRODUKT OF MAKIN AN 4.0 AN YR "+" MKAY
                
        BTW INTEGRATE STOCKS
        
        TIME R I IZ INTEGRATIN YR TIME AN YR 1.0 AN YR TIMESTEP MKAY
        INV R I IZ INTEGRATIN YR INV AN YR DIFF OF SHIPPIN AN SELLIN AN YR TIMESTEP MKAY
        WIP R I IZ INTEGRATIN YR WIP AN YR DIFF OF MAKIN AN SHIPPIN AN YR TIMESTEP MKAY
        
        BTW CHECK STOPPING CONDISHUN
        BOTH SAEM TIME AN BIGGR OF TIME AN SUM OF ZEND AN TIMESTEP, O RLY?
            YA RLY, GTFO
        OIC
        
    IM OUTTA YR SIMLOOP
    
    
KTHXBYE

YOU CAN RUN IT IN THE TUTORIALSPOINT ONLINE INTERPRETER, OR GET JUSTIN MEZA’S DESKTOP LCI.

SD INVENTORY LOLCODE.TXT

$lci main.lo
HAI, JWF!
SHOWIN RESULTZ FOR PRODUKSHUN
0.00 5.00
                    +
1.00 5.00
                    +
2.00 5.93
                        +
3.00 6.64
                           +
4.00 7.34
                              +
5.00 8.00
                                 +
6.00 8.62
                                   +
7.00 9.22
                                     +
8.00 9.78
                                        +
9.00 10.31
                                          +
10.00 10.82
                                            +
11.00 11.30
                                              +
12.00 11.75
                                                +
13.00 12.18
                                                 +
14.00 12.46
                                                  +
15.00 12.30
                                                  +
16.00 12.03
                                                 +
17.00 11.68
                                               +
18.00 11.29
                                              +
19.00 10.89
                                            +
20.00 10.51
                                           +
21.00 10.17
                                         +
22.00 9.89
                                        +
23.00 9.66
                                       +
24.00 9.49
                                      +
25.00 9.39
                                      +
26.00 9.35
                                      +
27.00 9.35
                                      +
28.00 9.40
                                      +
29.00 9.47
                                      +
30.00 14.56
                                                           +
31.00 15.91
                                                                +
32.00 14.12
                                                         +
33.00 14.07
                                                         +
34.00 14.45
                                                          +
35.00 14.73
                                                           +
36.00 15.01
                                                             +
37.00 15.27
                                                              +
38.00 15.51
                                                               +
39.00 15.75
                                                                +
40.00 15.97

I THINK THIS SHOULD BE A PART OF EVERY SYSTEM THINKERZ LITTERBOX TOOLBOX.