System Dynamics — a highly underrated discipline in Productivity
System Dynamics — a highly underrated discipline in Productivity
The butterfly effect is one of the more confusing concepts we have encountered; a flap of wings somewhere, and a tsunami develops halfway across the world. It is the nature of Nature to be unpredictable and grand in ways that are simply beyond us. Yet, the human mind, ever logical and methodical, seeks patterns, connections and rationales. Doing this in a step-by-step fashion is the premise on which the field of system dynamics was first developed at MIT in the 1950s.
What is System Dynamics?
System dynamics is a highly abstract method of modeling. It ignores the fine details of a system, such as the individual properties of people, products, or events, and produces a general representation of a complex system. These abstract simulation models may be used for long-term, strategic modeling, and simulation.
To set the context of why we’re discussing System Dynamics as a useful tool to develop contrarian thinking, let me give you a metaphorical situation to ponder over-
Think of a guy facing pressure from all sides of his organization to solve a particular problem; say- he’s a police officer who is given the task of dismantling a large crime syndicate based in a remote rural location. The syndicate rules with an iron fist and keeps all the officers in their pockets.
He’s at his wit’s end as to how he will “break the cage”, so to speak. The pressure from his company is staggering, and he makes a snap decision along the lines of the quote- “If you don’t like how the table is set, turn the table over.” He decides to organize a huge encounter to eliminate all the “suspects”, preparing a strike team impulsively, which gives no time to the “informants” to do anything about it.
All the gangsters are killed. The public is now extremely happy with the police department for solving the problem so decisively. The commissioner announces that he will offer his brave officer a promotion.
Now, his more methodical colleagues think that quick-draw solutions that break conventional rules are the only way forward. They too crack down heavily on the suspects they had been tailing for the past few months.
Barely a week later, the repercussions of his snap decision arise. Three of those who were killed were found to be civilians, simply in the wrong place at the wrong time.
When asked for an explanation, he will simply say- “Oh, it wasn’t really my fault. They seemed like they were about to shoot me.” He’ll try to get you to believe that he’s managed the situation perfectly. Worse news? He might actually believe it himself.
In reality, this simply shows how limited and narrow his outlook was, of the system which he is a part of (and supposed to be capable of managing and understanding).
Now, the management will accept his excuses, since they lauded him publicly, based on his supposed “out-of-the-box” approach and don’t want to be proven wrong. His fellow police officers will mistakenly think that such narrow thinking is permissible (since there were no consequences attached), and they continue to go down the spiral of shooting down anyone who looks at them funny.
Now, the ignorant public lives have to live in the shadow of fear. The tyranny of the badge & gun looms constantly over them. People might try to protest this arrangement on the grounds of human rights violation, and just the Black Lives Matter movement and biases of individual police officers, gruesome mistakes can be made. Eventually, the number of instances of falsified killings may rise to such a point that the police seem no better than the erstwhile criminals. The arrangement might become an impediment to law abiding citizens. There might be new waves of self acclaimed social justice warriors looking for “a solution” that end up being targeted as criminals themselves instead of being hailed as vigilantes by the police. The public might agree with their sentiment, a storyline that was portrayed in SpiderMan. A cycle of hatred could very much be on the cards. The civilians might then decide to take matters into their own hands, and the entire law and order situation in the region goes bananas.
Brilliant.
System dynamics arose as a discipline to understand the impact of policy changes on complex systems (within a corporation/ in a complete market for a single category of products (eg- the steel industry). With time, the need justified itself due to a very puzzling fact-
Problems in the world are becoming harder to solve, not despite our increasing cleverness and analytical abilities, but BECAUSE of it. We’re becoming too smart for our own good.
“Wait, that doesn’t sound right!
Shouldn’t things become easier as our understanding of the world evolves?”
While that seems logical, the issue is that we’ll simply find harder problems with time as a result of the solutions we implemented to solve our older, easier problems; i.e- solutions create more problems. But how?
The simple approach for solving problems looks like this-
Identify problem → Gather Data → Evaluate alternatives → Select solution → Apply
This is the open-loop approach, which is ingrained into our psyche from our school days (even skipping over the Gathering of data and evaluating alternatives; do you really need to do that for 2+2=4?).
The problem with this approach is the lack of incorporation of the side-effects that will arise as a result of our solution-
People fail to account for the multiple iterations of feedback incorporation and the possibility of the entire situation being misunderstood by them. Thus, reality needs a different approach, which has the perspective of the whole system in its structure. As you can see in the diagram below, it’s not a simple one directional process.
The decisions you make to achieve your goals will change the state of the system.
However, due to the fact that you cannot humanly account for all possibilities, your decisions will have unintended consequences; i.e- Side effects. These side effects will also change the state of the system.
Shortly after you take action, your opponents will also be affected by the change in the system, which will redefine their goals, leading to counter-strategies from their side, which will again produce different side-effects. This is a never-ending process.
The steps would change as follows after you apply your first solution-
Even this closed-loop approach has its fallbacks; the limitations of the human mind-
Decisions made by you/others are limited by your mental models (what you would intend to happen). You will not be able to account for all the possibilities.
Then, the effects and side-effects (a subset of the missed possibilities) would arise, which you have to repeatedly adjust with.
If the world changes to even slightly agree with your intentions, others will be motivated to bend the world to their motives as well.
The competing parties will strike back.
Side effects of their actions will come up.
For example, say you are a retailer in the fashion industry, and one of your products is a real hot-seller. You decide to raise its price, which then displeases the customers. Meanwhile, your competitors, who are on the lookout for an opportunity to sell more, cut the prices on products that are similar to yours. The customers buy from competitors, leaving you with new problems to solve. This situation could easily devolve into a long price war, with repeated adjustments and adaptation.
Thus, it is clear that we need to use all the tools at our disposal to account for an acceptable number of factors and outcomes, to generate a plan that is sensible for the system as a whole, and not just aggregating the intentions of its elements. As Tolstoy said in “War and Peace”-
Absolute continuity of motion is incomprehensible to the human mind. Only when he arbitrarily selects elements of that motion can he even begin to do so.
However, at the same time, a large chunk of human error comes from this arbitrary selection of elements.
The only way to avoid this error is to divide history into infinitesimally smaller elements; i.e- the individual thoughts of men which drive their decisions, integrate them as a whole and hope to arrive at the evasive laws of history.
Basically, what Tolstoy wants to say is that humans are limited by their inability to take all possibilities into the picture. While studying history for eg, then, they will err while choosing the “important” chapters, which will lead to incomplete, maybe even erroneous conclusions.
We can use a bit of high-school mathematics to explain this. As you can see below, the attempt to find the area under the curve using summation is incomplete (the white areas left between the curve and the blue rectangles. It is only when you use integration (the summation of infinitesimally small areas under the curve), can you account for the “complete picture”.
However, history does not follow the norms of mathematical integration. We are not exposed to a detailed view as it evolved over infinitesimally small time spans. And this is true for a screenshot of the present where we are only exposed to limited info to “capture” all future possibilities.
Thus, the only plausible way to correctly account for all the significant factors is to break down the system into optimally possible chunks (as small as you can make them without going berserk). This could be the plans and steps taken by people of note throughout the span of the time and place in consideration, which would then be added altogether. A very tall order, perhaps worth it with enough stakes riding on a correct decision?
Systems Dynamics can be explained easily by using its title-
System — Greater than the sum of its parts (Parts + side-effects)
Dynamics- “Only when changes of behavior arise in a system over time, do I get to explain it.”
Information collection is the basic step of the process. However, systems thinking will mostly require information on very intangible factors. Eg- For a retail chain, the name of the game is to increase sales volume → A very tangible variable. However, the factors which affect it are-
Most of the intangibles are standardized by using ordinal scoring (customer review forms) or set standards (FSSAI, BIS, etc.) However, in most cases, the intangibles will remain that way. Clearly, we need to know how to collect data effectively, while also ensuring that it is as genuine as it needs to be. We need to look at the various ways in which data will be encountered-
Characteristics of Information Channels-
Persuasiveness (Hard vs Soft data)- Easily quantifiable data is nominally persuasive. However, Sales inventory numbers won’t tell me about customer satisfaction (which is far more important, but soft).
Delay- There is almost always a gap between the instances of data collection, analysis, and decision formulation. This leads to a delay in the implementation of proposed solutions.
Distortion — Intentional modification of data for practical use (eg- Averaging the sales KPI (expand)- You won’t vary production every day based on daily sales.)
Bias & the 90/10 satisfaction rule.
When customer reviews are asked for, it is generally assumed that a majority of the satisfied customers will not do so, as they do not have any problems with the products. Only 5% or so post highly complimentary reviews.
However, about 10% will always have problems with the product and be very vocal about it.
The organization takes this 10% as bad apples and summarily ignores them. The 5% on the other hand, represent the good vibes from all of the 90% of the remaining customers. The company feels good about itself and continues to maintain the status quo, not making any changes.
Error- This is the least important part of the problem, but is the most heavily focused on. People would prefer to present an irrelevant but numbers-rich business plan, than a simple back-of-the-envelope scribble containing rare insights but no figures.
Crosstalk-
When a lost customer is asked to present his reasons for not buying the product to a customer care representative on phone, rather than explaining his struggles, he can simply say- “The price is too high.”
Even if the customer dutifully presents his problems, the representative might deem the customer as “finicky” and report to his superiors- “The price is too high.”
Even if the representative forwards the customer feedback transparently, the management, knowing that they can’t solve the problem, decide to simply lower the price :)
Clearly, it is no mean feat to successfully develop and deploy a sufficient model. As with any forms of product or art, the improvement has 2 primary factors — speed and frequency of iterations. What that means is having ways to get accurate feedback quickly and having a design and tools to rework it fast. Probably, it will take multiple iterations of feedback and solution design to be able to even remotely get to a long-term resolution. The speed at which these iterations are executed is very critical. Elon Musk’s management tip rules supreme- “Long is wrong, Tight is right.”
To design a plan of action using the tools of system dynamics, you will need to construct a linked model, listing all the factors that contribute to the changes in your variable of concern (sales/population/crop surplus…).
To do so, you need the cornerstones!
The building blocks of system dynamics are-
1. Causal linkages -
When events lead to changes in a system, some of them should qualify for being linked as cause-and-effect relationships (A happened, so B was inevitable).
Avoid creating a cause-and-effect relationship between two effects (health services and food are correlated but may be improved, not by each other, but perhaps industrial development).
Cause and effect relationships are mostly non-linear (eg- Input (cause) and output (effect) in industries).
Account for time delays with respect to the time horizon (10 hours (delay) don’t matter in 200-year horizons of human historical changes)
2. Feedback loops- positive vs negative -
Mathematically, multiply signs to find the nature (odd — signs = negative; even — signs = positive) of the loop. However, developing this understanding intuitively will serve you well.
A simple example-
The population is the state variable (it tells you how the system is performing).
More people → More births (+) vs More births → More deaths (+)
More births → More people (+) vs More deaths → Lesser people (-)
Hence, the left loop is positive, while the right loop is negative.
Positive loops are self-reinforcing. The higher your bank balance gets, the more interest is added to your balance, which results in higher interest on the increased balance amount, and so on.
Negative loops, on the other hand, have a strong touch of control around the status quo; i.e- homeostasis. Ex- More births could lead to a shortage of resources. Hence, deaths act as a control element for the ecosystem as a whole.
State variables (SVs) determine how the system will change; i.e- SV→ decision → changes system → New system changes SV → New decisions arise → So on…
Be very careful about the model you use. It should account for the most significant variables of the system, but not so many that you are unable to arrive at a decision within a reasonable amount of time. 95% efficiency with 10 variables is as good as 99% with 100 variables.
No human decision is made outside the context of a feedback loop → This is an axiom that has yet to be proven wrong. There are no explanations for this one, just plain, simple intuition. If you can find one such situation, you might become a very famous person…. Who forgot to think ;)
3. Rates & Levels
Levels are the State variables/ State of the system → What helps in making a decision
Rate- Decisions for change → Plans for bringing levels to the desired value
4. Structural-behavioral relationships
The structure of the system affects its behavior.
A positive loop will experience exponential growth/decline (Factor of c*e^(+/-x))
As employee performance increases/decreases, the supervisor becomes happier/angrier and supports/de-motivates his juniors in their further endeavors, producing a better/worse output than earlier.
Negative loops will pan out (factor of (1-e^(-x)))- the closer you get to your goal, the slower you will approach it (lack of resources).
As the temperature approaches the desired level, the air conditioner will decrease the compressor’s running time and fan speed.
There are innumerable nuances to this field, so we’ll let you explore them yourself. In the end, a capable systems thinker can-
Build a causal loop diagram that lists the requisite number of factors that affect his state variable of interest-
Examine how the causal factors have an impact on their linked effects (positive vs negative behavior → Linear vs nonlinear → Exponential vs logarithmic… and so on)
Finally, he/she should be able to combine all these behavioral tendencies and arrive at the ruling model-
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In conclusion, the aims are to-
Elicit and articulate mental models (what you intend to happen) and the impact of the socio-organizational structure.
Expand mental models by explicitly accounting for feedback.
Test and improve mental models and structures by using this feedback.
Design mental models for shared/group thinking (networked thought), which help you operate an organization more effectively.
Let’s finish with a rather crucial side note. In our pursuit of solving complex problems, sometimes we experience a very strong urge to simply slam our head on the table and blame all this malarkey on the system you operate within. Take the fault with the “Government” or “education systems” for example. To objectively and suitably deal with these cases, we have to be acutely aware of a rather “legendary” phenomenon is cognitive neuroscience :
The Fundamental Attribution Error.
This well established fallacy of the human brain, also known as correspondence bias or attribution effect, is the “tendency for people to under-emphasize situational explanations for an individual’s observed behavior while over-emphasizing dispositional and personality-based explanations for their behavior.” Let’s take a quick example.
As stated earlier, problems arise from both within and outside your locus of control (in this case, your system). So a decent table tennis in China will feel rather small and “defeated” simply because the amount of focus the culture has in the sport. This results in each district having potential Olympic Level players — players that will have a lucrative successful international career had they been in almost any other country. The limited team size means a lot of inspired, talented and hard working people are left out.
Does that mean they aren’t deserving of recognition? Not at all, they are just a big fish in a BIG pond. On the other hand, that same player can switch to a small population country with limited cultural focus on table tennis and flourish to a degree of privilege. Does it mean that this player would NOT deserve the success he might achieve in this environment? Will he/she be right to “attribute” their success to their personality traits rather than the situational context? Is it really “fate”?
Fundamental ATtribution Error ⇒ FATE
Fate itself is a very poignant instance of FATE: blaming some intangible, supernatural power for everything that goes wrong in this world. You can either work on what you can control or simply let the world throw you around at its turbulent whimsies. The best way to take control — applying System Dynamics. Your move, chief.
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