– A sidenote on Pizza Hawaii –
In our daily lives, we are confronted now and then if we should do things differently. Even when we speak about benign things like the maintenance of the car and a friend suggests a different way of cleaning it. When we bring food to a party and discuss different ingredients for the same dish. Aside from the discussion about whether or not pineapple belongs on a pizza, how do you objectively determine if it is good to progress, adopt new methods and ideas? In terms of culinary progress, it is evidently easy. Either you like it or not and hence, you progress or not. But when you take decisions on a different level it becomes less simple.
How do you determine if the benefit is worth the effort of restructuring a traffic system of a city or the educational program for a whole country? Discussions on this scale tend to consume a lot of time and energy from people involved and are inherently slow. But how come that big companies determine if it is worthwhile opening up a new store in a city district on a day to day basis? How do traders at the stock market make decisions about the outcome of investment in a matter of seconds and minutes?
Even hundreds of years ago, farmers had to cope with the questions of investment. When considering buying additional livestock, you are confronted with spending more resources and advancing your stables with the prospect of gaining a higher income. Although there might be enough income already, people tend to strive for growth. Hence, these farmers are outweighing the financial effort against the prospective income. As basic as this sounds even today in modern prediction models the same old principle is at work. This process of outweighing effort against benefit can be perceived as a very simple question. What is the minimum effort, that I can undertake, to achieve my goal? The answer to this question then gives us a value or a guideline when or how we should progress.
So what we have is a like a linear system with a number of variables that, if solved, yields the value we require to make our decision. And exactly this was the approach by Georg Danzig in 1947. He developed the so-called “Simplex-Algorithm” that is capable of solving such questions with a limited amount of iteration steps. Thereby a complex problem can be disassembled into several variables with different impact factor and processed by this method.
These simplex-algorithms as a subclass of linear optimization processes are essential for the prediction of economic development of whole countries as well as freight transport and management on a global scale. With modern technology, these models will be able to suggest the quickest, cheapest or even the most environmentally friendly way of transporting goods, depending on what you prioritize. Since the establishment of such prediction methods a lot of research, development and refinement produced a wide variety of models using linear optimization, heuristics or even randomization.
This is not surprising considering that some questions are just more complex than others. Take for example the paper making industry, where a product can have so many different specifications: material, thickness, size, binding, coloration, surface processing, water sign, and many more options. On top, the manufacturing process is also quite individual depending on the specification of the desired product. There is simply an incredible amount of combinations and therefore variables which to account for.
Therefore, such complex problems have to be divided in to groups of problems and sub problems which takes more time and resources to be solved. Besides, straying from the linear dimension of the non-linear optimization enables solving highly complex systems. This however, can reach a level where people can not even trace back all logical decisions as is already the case for the use of artificial intelligence in stock market decisions. These AIs take a known working approach and refine it to the maximum even for complex systems. On the other hand, a lot of man-made systems also work by this principle. The scientific community in itself is thriving from optimisation and advancement aside from answering fundamental questions.
One field that resists vigorously against all approaches for mere rational optimization however is food. What was formerly known as space food (a dry powder that contains all necessary nutrients) is now commercially available in a wide variety.
But all of these mixtures, that claim to contain all the nutrients in the perfect balance that we need, have one problem in common. The balance of nutrients is based on scientific findings which are just an average and do not account for everyone and they are not definite. There may be nutrients we need that we have not identified yet. Without the addition of aromas they often do not taste even if our taste buds might register the presence of all our required nutrients. On the other hand, the taste is highly dependent on nutrients. This makes food a highly complex system with a lot of variables to atone for.
There is a place that just is not always so rational when it comes to decision making and that is our mind. Therefore, it is not surprising that Pizza Hawaii, which can even be nutritionally favorable over some other pizzas, is not as popular. Even when considering more variables as pricing, it is a famous example of the irrationality of human decisions, since we simply have different taste and do not always make decisions on a mere objective basis. We base decisions on values we establish for ourselves and a tradition that ensures a good taste might just be more important than experimenting with ingredients on a pizza. This is exactly the weak point of such prediction models since an algorithm might be perfectly capable of suggesting to us the perfect company, living place, and even partner but still fails to really grasp what we expect from life on a personal basis.
In principle, we can say it is always good to embrace progress, but in some regards, it is quite acceptable to stick to your old guns. We can use logical tools like the simplex-algorithm to help us determine the course of very complex systems like governing traffic. But these tools can not ultimately solve the question of what we should eat or how we want to live together as a society. Let alone, what equality means and how we ensure it. These are complex questions that we have to answer the old way by time-consuming but worthwhile debates.
— Kevin Machel
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