complexity stems from intricate interdependencies of which there are many, many examples in our increasingly interconnected world. Sometimes it is possible to reduce dynamic complexity by making interdependencies less entwined and more understandable. Indeed, this goal of simplification is really the ultimate aim of policy design in system dynamics – redesigning social and business systems so that, despite their complexity, normally-competent people can run them successfully.
Why are fisheries so dynamically complex? What changes would make them less prone to sudden and catastrophic decline? Herein lies the whole area of fisheries policy involving fishermen, fishing communities, governments, marine scientists, consumers and fish themselves. There is a lot that could be modelled about the interactions among these stakeholders and arguably a serious fisheries policy simulator would be much bigger and would involve many more variables and relationships than those in our small Bonavista model. Nevertheless, at the heart of any such model will be a representation of the factors – biological, economic, political and social – that determine the balance of ships at sea and fish in a commercial fishery.
A vital part of dynamic complexity in fisheries lies in the relationship between the catch and fish density. Not surprisingly, if the fish density is very low then it is difficult for fishermen to locate fish and the catch is lower than normal. But the relationship is non-linear as shown in Figure 1.11. Here, fish density is measured on a scale from zero to one, where one is the highest possible density (the number of fish is equal to the carrying capacity) and zero is the lowest (there are no fish). The vertical axis shows the effect of fish density on catch per ship, also on a scale from zero to one. In our imagined Bonavista, the normal catch per ship is 25 fish per ship per year – remember this is a scale model. The actual catch per ship is obtained from the product of normal catch (25) and the effect of fish density.
Figure 1.11 Relationship between catch per ship and fish density
When the fish density is high, in the range between 0.7 and one, the catch per ship is stable at 25 because there is little or no depressing effect from fish density. The sea is full of fish and they are easy to find and catch. When the fish density is lower, in the range 0.4 to 0.7, the catch is still very close to normal (25). The assumption, borne out empirically in real fisheries, is that fish are still quite easy to find even when there are fewer, because they tend to cluster. Only when the fish density falls very low, in the range between zero and 0.4, does scarcity make fishing more difficult. In this narrow range the effect of density falls swiftly from 0.9 (almost normal) to zero.
The non-linearity, the sudden depressing effect of density on the catch, makes fisheries management difficult. You can appreciate why if you imagine the argument between a marine biologist and a fisherman about the need to conserve stocks. When the fish population falls to half the maximum (fish density equal to 0.5) the marine biologist argues that stocks are too low. But the fisherman reports (accurately) there is no difficulty catching fish, so what's the problem? In all likelihood, the fisherman thinks the fish stock is actually higher than the marine biologist's estimate. The biologist is exaggerating the problem, or so it seems to someone whose livelihood depends directly on the catch. When the fish population falls to one-quarter of the maximum (fish density equal to 0.25) the marine biologist is frantic and even the fisherman is beginning to notice a reduction in the catch, down by about one-third relative to normal. That outcome, though worrying, is not obviously fatal. Perhaps with a bit more effort and luck the poor catch can be rectified, and why believe the marine biologist now, when he/she was seemingly so wrong and alarmist before? The non-linearity creates confusion in the attribution of causality – what causes what in the system – and such confusion is a typical symptom of dynamic complexity.
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