However, in year 25 something of vital significance happens under water, hidden from all but the fish themselves. The number of new fish per year (line 2) peaks and then starts to decline. This is the first evidence, a kind of early warning signal, that the fishery is being overfished. Fish density is now so low that regeneration is suppressed. The fishery teeters on the brink of catastrophe. The rate of population decline (the steepness of line 1) increases. But the catch keeps on rising throughout year 26 so no action is taken to curtail fleet expansion. In year 27 the catch itself peaks and then declines, gradually at first. This is the first tangible evidence of stock depletion underwater, but even so the signal is likely to be ignored until the trend proves conclusive and until the fishing community persuades itself to limit fishing. In the simulator, we assume that new ship purchasing continues apace until year 30. By then the fish stock has fallen to around 400, only 10 % of the maximum fishery size. The regeneration rate (new fish per year) is still in decline and far below the much reduced catch. Measures to halt investment and to idle ships in years 30 to 40, drastic though they are, are too little too late. Bonavista's fish have all but gone and with them the industry on which the community depends. By year 35 there are so few fish left (only 16!) that, even with a total ban on fishing, it would take two decades to rebuild the stock to its value in year 10 when our imagined Bonavista first began commercial fishing.
Now you are familiar with the gaming simulator, you can use it to test alternative approaches to growing and developing the Bonavista fishery. First press the ‘Reset’ button to obtain a new blank time chart and to re-initialise the simulator. Next, without altering either slider, press the ‘Run’ button twice in order to simulate 10 years of natural growth in the fish population so that Bonavista inherits a well-stocked fishery. Then re-simulate the same fleet expansion as before – two ships per year for years 10–25. You will find yourself back in Bonavista's heyday with a fleet of 30 ships and a history of 15 years of steady growth in the catch. Now it is your responsibility to steer the community toward a sustainable future that avoids the errors of the past. For realism you may, as before, want to ‘grey-out’ the trajectories for fish stock and new fish per year. What is happening to the fish stock underwater is difficult to know, vague and often subject to controversial interpretation. Also bear in mind the practical political difficulties of curtailing growth and of idling ships in a community that depends on fishing. Think about plausible adjustments to the two sliders at your disposal. It is a good discipline to note your intentions, and the reasoning behind them, before simulating. Imagine you first have to convince the Bonavista community and fishermen to adopt your plan. Then, when you are ready, simulate, analyse the trajectories and try to make sense of the outcome. Was the result what you expected? If not then why? If you don't like the result then try again.
Although in principle it is possible to create a sustainable Bonavista it is very difficult to do so in practice or even in a simulator, particularly when you inherit a fleet of 30 ships following 15 years of successful economic growth. The fisheries simulator is one example of a dynamically complex system, of which there are others in this book and many more in life. Often such systems give rise to puzzling performance through time – performance far below the achievable and, despite the best of intentions, not what people (stakeholders in the system) want. In this case, the fishery is prone to catastrophic decline when perhaps all that fishermen desire, and the fishing community wants, is growth, more and better ships, and a higher standard of living. Dynamic complexity stems from the connections and interdependencies that bind together social and business systems. When a change happens in one part of the system (e.g. more ships are purchased) sooner or later it has implications elsewhere, and vice versa. Moreover, these implications are not always obvious and are often counterintuitive (e.g. more ships can lead to a greater rate of fish regeneration, but not always).
Dynamic complexity does not necessarily mean big, detailed and complex, involving hundreds or thousands of interacting components. Indeed, as the fisheries simulator shows, dynamic complexity and puzzling performance can arise from only a few interacting components. What matters is not so much the raw number of components but the intricacy with which they are bound together.
Such intricacy involves time delays, processes of stock accumulation (such as the accumulations of ships and of fish), non-linearities (such as the hump-shaped relationship between fish density and fish regeneration), and closed feedback loops (such as the reinforcing relationship between fish stock, fish density, fish regeneration and fish stock). These special terms, the language of feedback systems thinking, will become clearer later. For now it is sufficient to appreciate that dynamic 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.