The Systems Dynamics “Beer Game” seems like a useful example of how something like (but not the same as) an info-cascade happens.
https://en.wikipedia.org/wiki/Beer_distribution_game—“The beer distribution game (also known as the beer game) is an experiential learning business simulation game created by a group of professors at MIT Sloan School of Management in early 1960s to demonstrate a number of key principles of supply chain management. The game is played by teams of at least four players, often in heated competition, and takes at least one hour to complete… The purpose of the game is to understand the distribution side dynamics of a multi-echelon supply chain used to distribute a single item, in this case, cases of beer.”
Basically, passing information through a system with delays means everyone screws up wildly as the system responds in a nonlinear fashion to a linear change. In that case, Forrester and others suggest that changing viewpoints and using systems thinking is critical in preventing the cascades, and this seems to have worked in some cases.
That’s a really interesting effect, thanks for linking. I have two questions:
1) I’m confused about what the mechanism that produces the Bullwhip effect is.
One video suggested the following: as demand rapidly increases during time_step_1, suppliers aren’t able to fully adapt and meet it, which causes an even larger shortage during time_step_2 and hence even larger demand; and somehow these effects compound down the supply chain.
Another mechanism is just that the demand signal is noisy, and so its variance will increase as one moves down the supply chain. But I’m confused why this causes everything to blow up (as opposed to, say, different sub-suppliers making errors in different directions, which sorta cancel out, even though the larger variance at the end-supplier causes some volatility. That is, it’s just as likely that they underestimate demand as it is that they overestimate it.)
2)
changing viewpoints and using systems thinking
Exactly what does this imply that I, as a middle-manager somewhere in the supply chain, observing a noisy demand signal, should do? How does this concretely change my order decision from my supplier, in a way which improves things?
1) It’s neither noise nor rapid increase—it’s delayed feedback. Control theorists in engineering have this as a really clear, basic result, that delayed feedback is really really bad in various ways. There are entire books on how to do it well—https://books.google.ch/books?id=Cy_wCAAAQBAJ&pg=PR9&lpg=PR9 - but doing it without using these more complex techniques is bad.
2) You either hire a control theorist, or (more practically) you avoid the current feedback mechanism, and instead get people on the phone to talk about and understand what everyone needs, as opposed to relying on their delayed feedback in the form of numeric orders.
We (jacobjacob and Benito) decided to award $50 (out of the total bounty of $800) to this answer.
It offers a practical example of a cascade-like phenomenon, which is both generally applicable and has real economic consequences. Also, the fact that it comes with a came to understand and practice responding is rare and potentially quite valuable (I’m of the opinion that deliberate practice is currently a neglected virtue in the rationality/EA spheres).
The Systems Dynamics “Beer Game” seems like a useful example of how something like (but not the same as) an info-cascade happens.
https://en.wikipedia.org/wiki/Beer_distribution_game—“The beer distribution game (also known as the beer game) is an experiential learning business simulation game created by a group of professors at MIT Sloan School of Management in early 1960s to demonstrate a number of key principles of supply chain management. The game is played by teams of at least four players, often in heated competition, and takes at least one hour to complete… The purpose of the game is to understand the distribution side dynamics of a multi-echelon supply chain used to distribute a single item, in this case, cases of beer.”
Basically, passing information through a system with delays means everyone screws up wildly as the system responds in a nonlinear fashion to a linear change. In that case, Forrester and others suggest that changing viewpoints and using systems thinking is critical in preventing the cascades, and this seems to have worked in some cases.
(Please respond if you’d like more discussion.)
That’s a really interesting effect, thanks for linking. I have two questions:
1) I’m confused about what the mechanism that produces the Bullwhip effect is.
One video suggested the following: as demand rapidly increases during time_step_1, suppliers aren’t able to fully adapt and meet it, which causes an even larger shortage during time_step_2 and hence even larger demand; and somehow these effects compound down the supply chain.
Another mechanism is just that the demand signal is noisy, and so its variance will increase as one moves down the supply chain. But I’m confused why this causes everything to blow up (as opposed to, say, different sub-suppliers making errors in different directions, which sorta cancel out, even though the larger variance at the end-supplier causes some volatility. That is, it’s just as likely that they underestimate demand as it is that they overestimate it.)
2)
Exactly what does this imply that I, as a middle-manager somewhere in the supply chain, observing a noisy demand signal, should do? How does this concretely change my order decision from my supplier, in a way which improves things?
1) It’s neither noise nor rapid increase—it’s delayed feedback. Control theorists in engineering have this as a really clear, basic result, that delayed feedback is really really bad in various ways. There are entire books on how to do it well—https://books.google.ch/books?id=Cy_wCAAAQBAJ&pg=PR9&lpg=PR9 - but doing it without using these more complex techniques is bad.
2) You either hire a control theorist, or (more practically) you avoid the current feedback mechanism, and instead get people on the phone to talk about and understand what everyone needs, as opposed to relying on their delayed feedback in the form of numeric orders.
We (jacobjacob and Benito) decided to award $50 (out of the total bounty of $800) to this answer.
It offers a practical example of a cascade-like phenomenon, which is both generally applicable and has real economic consequences. Also, the fact that it comes with a came to understand and practice responding is rare and potentially quite valuable (I’m of the opinion that deliberate practice is currently a neglected virtue in the rationality/EA spheres).