First we teach the computer, then we learn from it
The circle of advancement is when master teaches the beginner in hope that the latter will overgrow the teacher. Similarly, a human has created the smart algorithms for the computers attempting to include all possible information in the decision-making process. Now, it is time to apply the existing knowledge to the personal life.
to live by
The book by Brian Christia and Tom Griffiths, Algorithms To Live By, develops an idea how to implement the computer scientist approach to the life decisions. The rational emotion-dry perspective of a computer scientist or mathematician can help human with various tasks from organizing the book shelf to choosing the partner for life. As the authors state, the main argument of the book is that "there are deep parallels between the problems that humans face and those considered by mathematicians and computer scientists."
Consider any decision-making situation: there is the constraint of time, primary knowledge, assumptions, intuition or probability of the events, and certain payoffs - just like in a math task. Of course, the mathematical formulae is not the way to chose the restaurant for the evening. However, knowledge of the theoretical side of the solution will help a person to make the decision faster and more reliable.
what counts the most
The book tells about eleven types of provable optimal strategies that can be applied to the human life. One of the examples is the optimal stopping point. When to stop looking for a new house? Which parking place to take? The rule of 37 percents helps to ease the decision in quantified and rational manner. A person considers a pool of options, thinking hard which one he or she likes the most goes with that one. In many life situations, there is no luxury of being able to get back to the rejected option. So-called a one-shot game.
Similarly to the sufficiently overcrowded real-estate market, a person should choose not only which house to buy but also how many options to consider. There is a need to gather intel, make the market overview. But how to know when there is enough of information in possession? When to stop, make a choice, and, eventually, buy the house being sure that it is the best option? Mathematicians say that the magical number is 37%. Picking third of the options, checking them out, and going for the next one which is better than the previous one is the best strategy. At the same time, this approach is used by computer scientists.
In similar fashion, people choose the spouses. Just imagine to look for the second half the whole life and one will never find one. There has to be a limitation and will to settle down. The cost of the search for the partner grows with the amount of time spent. Therefore, when the assumption is that person wants to find a spouse before turning forty, the one better than others met before he or she turns 26 will be an ultimate marriage choice. The book presents more of those useful strategies, like "constraint relaxation", "catching", implementation of "Bayes rule", and others.
To some extend this strategy can be seen in the way people google. According to Google Organic CTR Study 2014, on average, more than 70 percent of the searches are activated on the first page of the google search. The first five search result on the first page get about 67 percent of click. The rule of 37 percent works intuitively and is provable via statistics. Moreover, this strategy becomes even more useful when the amount of data and knowledge is constantly growing. Google algorithms get autocorrected and evolve with the users' searches; human decision-making process might be advanced just by implementing the similar strategy.
Rationalization of the users' choices by the firms as well as a rising of customers' level of education and science popularization, shall lead the humanity to the smarter decision-making. Even when a client is not sure about the options, there will be a well-written algorithm helping to make the most suitable choice. This provokes questions of machine advancements and the future of the workforce. One fact shall be in mind, even if there is a possibility, and many scientists are working on making it happen, while computer algorithms will evolve to the point of surpassing the human judgment, the human creative decision process will remain superb. And not to allow computers to outsmart people, humanity has to learn algorithms and math science.
How does the
robot mind work?
Computer scientists are good at writing the algorithms, which get more complex with adding new assumptions. Eventually, the way how the robot thinks becomes the secret for the code writer himself. MIT labs have come up with the smart visual solution to understand and trace the decison-making process of the dron. This innovation will enhance the autopiloted and driverless solutions for future technologies.
Computer science of building the algorithms, calculating probabilities and payoffs, will enter the everyday human decision field in the nearest future. The popularization of math and other science fields, as well as vanishing of mechanical jobs, will effect and evolve to more rational and sustainable decision-making. More intelligent clients will require smarter solutions.