Today everywhere we look we confront chaos. All anyone seems able to do is argue and try to manipulate each other. People all over the world are rising up against their governments that are no longer capable of solving their problems. Congress is in gridlock. Why is this happening?
With rising technologies and their intertwined and global impacts, we are now capable of creating situations that are too complex for us to understand or control using our innate capabilities. When we are no longer capable of solving our problems, fantasies replace reality.
However, financial institutions are doing well. They can create situations for which they may not know the consequences, but they don’t care because it’s a gamble. They keep any gains, and use their money to manipulate government to cover their losses. This is costing the taxpayers and widening the wealth gap.
Our innate ability to use cause-and-effects has allowed us to survive in the past. It is a gift of evolution. But now we are in over our heads. What can we do about this?
We need to move beyond our innate capabilities in order to deal with more complex systems of cause-and-effects than we have ever had to deal with in the past. We now need the help of the computer to cross the boundary between the complexities we had been able to deal with and the much more complex situations that we are facing today.
A method for dealing with these complex problems has been developed and a computer program to implement it is now being developed.
Using this program as developed so far, along with extensive hand manipulations, we, the program and I, have been able to gain significant insights into such complex social problems as trying to better understand the causes of the economic crisis and widening wealth gap and what we might do about it. One contributing cause we found was that in negotiations, a party that has the information he needs to understand his own self-interests has an unfair advantage over the other party who does not have the information he needs. Adam Smith said that if people negotiate in their own self-interests, we will have a fair capitalist system. People today either forget the assumption that all parties equally have the information they need in negotiations, or have used this assumption maliciously. Think of the subprime mortgage negotiations.
When the program is finished, what are now hand manipulations will be built into the program.
The method uses cause-and-effects. Given the cause-and-effects and an effect, i.e. behavior, to be explained, one can begin with the behavior and trace backwards through the cause-and-effects to find explanations for why that behavior occurred.
An explanation for a behavior is a set of assumptions that if true would cause that behavior. Finding explanations for observed behaviors is the most fundamental problem in science.
People are quite good at finding the cause-and-effects that underlie a situation. But they are dreadful at logically putting together enough of these cause-and-effects in complex problems to understand their implications. The program helps people with various perspectives on a situation to collaborate in raising and discussing the cause-and-effects.
Then given a behavior to be explained, the program does the logic to string together the appropriate subset of these cause-and-effects to find all the possible explanations for that behavior This is logical abduction.
Any behavior will usually have multiple possible explanations. Then we must consider which of these explanations is the most likely one.
First, we can eliminate explanations that would also predict behaviors that do not occur. Each of these possible explanations will predict a multitude of behaviors other than just the one to be explained. Some of these explanations are likely to predict behaviors that can be shown to be false. So it is necessary to find for each possible explanation all the behaviors that it would predict (logical deduction), test these predictions to see if they are true, and eliminate those plausible explanations that predict any behavior that is not true.
Think of how a physician diagnoses her patient’s symptoms. First, she thinks of all the possible explanations, i.e. diagnoses, that would predict those symptoms (abduction). Then she considers for each proposed diagnosis what other symptoms the patient would have if that diagnosis were correct (deduction) and tests to see whether these other symptoms also occur. If not, she rules out that diagnosis. This is basically the same process we discussed above.
Thus, this program can be used to diagnose a patient’s medical symptoms. If a panel or a computer such as Watson were to keep up with the latest literature and maintain the cause-and-effects, physicians who may not be able to keep up with all the latest literature could subscribe to this updated knowledge. This could reduce diagnostic errors and reduce the costs of unnecessary tests made only to protect themselves from malpractice suits.
Since this method and its computer program can be used to solve a very fundamental problem, it has many possible applications. It can be used to find faults in systems whose proper behavior has been described by cause-and-effects. By finding the explanations for a desired system’s behavior, one can turn the assumptions that explain that behavior into designs, i.e. actions, that would achieve that behavior. Or by using cause-and-effects and some criminal evidence, one can determine what further evidence would be needed and eventually lead to determining the culprit. This can also be used to interpret what might have caused certain archeological evidence, or what might have caused certain events in history. If the proper behavior of the reactor at Fukashima had been committed to cause-and-effects, those handling the disaster would be quickly alerted to what to do and what not to do.
We will be concerned with mechanisms. The effects have values associated with them. These associated values are either increasing or decreasing. If increasing, we declare the effect to be ‘positive’, and if decreasing, we declare it to be ‘negative’. If A causes its same consequence on B, we call that cause-and-effect ‘positive’. If A causes the opposite consequence on B, we call that cause-and-effect ‘negative’. Thus, the cause-and-effects can be shown on a diagram where the cause-and-effects are arrows labeled either positive or negative, and the nodes are effects that are labeled either positive or negative.
A unique feature of this method is that it can deal with cause-and-effect circuits. If A causes B, B causes C, and C causes A, this is a circuit. I have studied cause-and-effect circuits and have developed some interesting ways of handling them.
Normally, there will be either one or two solutions for a circuit. If we have a circuit where A causes B and B causes A, we have only one solution if there are an odd number of negative causes in the circuit. This implies that increasing the value associated with an effect in the circuit will cause that value after going around the circuit to decrease. This is the classical feedback loop that tends to hold the values in the circuit constant.
If the circuit does not have an odd number of negative arcs, it can have two solutions. We have one solution if A is made positive by an outside cause, and another solution if A is made negative by an outside cause.
We use the two solutions to separate a problem into two problems. One problem includes the circuit and all the outside causes that would make A positive, and the other problem includes the circuit and all the outside causes that would make A negative.
These two versions of the problem fight against each other. If one problem produces what we consider to be a vicious cycle, the other problem will produce what to us is a virtuous cycle. So we want to introduce causes external to the circuit that give the virtuous cycle the advantage over the vicious cycle.
The two solutions come from the assumption that when a positive A causes a positive B, then also a negative A causes a negative B. They do not come from the assumption that A causes B implies that B must cause A, i.e. that a cause can change direction. That assumption is not allowed for mechanisms. So we get two problems even though the directions of the causes never change.
The arguments we just made concern circuits. But in practice, we will work with blocks that contain one or more circuits. Every circuit is in just one block. But the rules that pertain to circuits have equivalent rules that pertain to blocks.
Consider the following problem, one of many that have been analyzed using this method, and a possible solution that it suggested. To understand it properly, one may have to go through it carefully step-by-step.
- Big businesses do not invest in small businesses because they do not believe the small businesses would be profitable.
- Many small businesses would be profitable if they had the investments they need.
- Uncertainty about the future would cause big businesses to hoard cash rather than invest it.
- Government guarantees of the investments by big businesses in potentially profitable small businesses would cause these small businesses to be profitable.
Note that “1. Big businesses do not invest in small businesses because they do not believe the small businesses would be profitable” causes “2. Many small businesses would be profitable if they had the investments they need”, which in turn causes 1. This forms a circuit. 1. causes 2. and 2. causes 1.
Note that “3. Uncertainty about the future would cause big businesses to hoard cash rather than invest it” would cause the circuit to spiral down so that both big and small businesses and the economy as a whole would suffer. This is a vicious cycle.
But note that “4. Government guarantees of the investments by big businesses in potentially profitable small businesses would cause these small businesses to be profitable” would drive a virtuous cycle where big businesses and small businesses alike and the economy as a whole would prosper. This suggests that the government should consider a policy to guarantee the investments by big businesses in potentially profitable small businesses.
Would this have been considered if it were not suggested by this analysis? There is no evidence that I have seen that such a policy is being discussed. There are many other such problems where this method of analysis may suggest likely solutions that do not seem to be currently under consideration.
We could go a long way toward resolving some of the complex social problems we face today if such an approach were used. Unfortunately, when something new becomes possible, it may take a long time, perhaps decades, before it is recognized and put to use. Can we afford such a delay while our problems continue to get worse?