Making predictions is an active form of decision-making people do all the time mostly without even being aware. When the bell rings, we guess that there is someone behind the door waiting for us to come. Producers try to predict the response of their customers in the face of different price policies and the people who cook try to estimate the time of their meal being ready; such examples fall into category of the science of forecasting. Forecasting problems can be related to weather, economy, business and other fields. Such problems mostly include many dependent and independent variants. Reducing risks through forecasting and related issues make the science of forecasting more popular in our time. For example experts' advise on how to make wise investments or a few days' weather forecast constantly have their place in media. Even though people make use of their ability to predict repeatedly in everyday life, they are mostly unaware of how decision processes take place.

In spite of the developments in science and technology, we are still so powerless at predicting phenomena; there is so little we can control and uncertainties are so many. We do not face the challenge of uncertainties at predicting happenings but also at decision-making. While we make decisions in daily life we try to minimize uncertainties. Any decision that holds uncertainties in its nature is risky. Therefore, decision-making processes are a kind of risk management at the same time. All the efforts are directed toward reducing the possible risks and mistakes. High-risk problems have a higher ratio of mistakes.

Studies of forecast need collecting numeric data and analyzing them along with obtaining relevant information based on observations. The data to be collected should be objective and the statistical methods appropriate. Otherwise, unsuccessful forecasts will lead to waste of time, money, and resources. Decision problems with uncertainties can be of different nature. For example, it is possible to reckon when an apple which broke off from its branch will reach the ground by knowing physical laws. As factors get more complicated, possibility of surprises is much higher. The discipline which systematizes these studies is econometrics. Statistics, mathematics, economics, optimization, decision-making, and computer science fall within the studies of econometrics. Forecast problems are mainly studied with numeric and extrapolative analyses.

In recent years, intuitive methods have been added to these two major approaches. Along with other issues, numeric analysis is frequently used in artificial neural networks, computer simulations, and computer-based learning methods. Artificial neural networks are formed by trying to model the millions of neurons in human brain work. The artificial cells whose number varies between five to ten are educated by using a certain mathematical transformation function, with recurring algorithmic processes. At the end of the process of education with past data, the performances of the calculations in new situations are evaluated. This method, which is used at various forecast problems, is really meaningful in terms of reflecting what can be done by merely copying a few of the millions of human nerves. However, the greatest obstacle before the studies of artificial neural networks are the computers with insufficient capacity. Even super computer systems have difficulty in an optimization problems with more than a hundred cells and the data obtained is far from useable. One cannot help but amaze at the perfect capacity of human brain and how it tackles hundreds of parallel processes incessantly. In some decision-making problems, due to insufficient numeric data, the parameters targeted to be forecast may not be reliable and present a peculiar nature. The studies about very rare diseases and forecasting technological developments are examples to that. In such cases, numeric analyses do not help because of not having any reliable numeric data. Therefore, extrapolative analysis methods are preferred at such forecast problems. At forecasts based on extrapolation, the expert's level of knowledge, intuition, experience, capacity of processing information, and power of judgment are important but they remain limited. There are lots of forecast problems about socioeconomic life such as economic fluctuations, price moves, and commercial size. Healthy forecasts on how and when to make investments are very important in order for the investors' determining how to utilize their resources. However, the investors' decisions will inevitably have uncertainties to a great degree. Therefore, it can be said that studies of forecasting are still in the cradle.

No matter of what quality, forecasts are always needed, since expectations for the future need to be determined and met. One of the basic assumptions of the economic system that make its effect felt in the global scale is that, human beings are self-centered creatures who act on opportunist drives. Such philosophies have always impelled people to be egocentric. However, people can listen to their conscience instead. They can simply choose to be altruistic; they can choose to care about others and help them. It is possible to reverse such an understanding of selfishness by giving charity and fulfilling other responsibilities. Thus, the forecasts about the future depend on what is to be given priority. In other words, the science of forecast can serve a more humane philosophy.

Everything is finely balanced with appropriate measures in this universe. Everything is created with wisdom. From the perspective of forecasting studies, our efforts can in a way be seen as guesswork to learn about destiny. Although we can try to make predictions within certain limits, we can never ignore possible surprises which take place totally out of our control. Human beings are equipped with an ability to make decisions in the face of uncertainty. We are supposed to comprehend the philosophy of creation and try to discern the meaning of the pattern being woven by Providence.

Ertugrul Deniz is a freelance writer from Turkey with a PhD degree in mathematics.

References

• Makridakis, Spyros; Wheelwright, Steven; Hyndman, Rob J. (1998). Forecasting: methods and applications, New York: John Wiley & Sons.
• Fama, Eugene (1970). "Efficient Capital Markets: A Review of Theory and Empirical Work". Journal of Finance 25 (2): 383–417.