A unified theory of social interaction
The sun is out bright. There is a light breeze in the air. Fresh. About half the leaves on trees have turned orange. Its a crisp fall morning. I have just had my cereal. Its this nice warm and satisfied feeling inside me that makes me want to do better things in life. So I've decided to take some time and write something meaningful for a change.
P A R T - 1 .[Abstract]
This essay attempts to construct a logical and mathematical framework for modeling and studying social interaction patterns. It aims at allowing meaningful decision-making for the purpose of optimization of social participation. In this endeavor, it makes use of well known tools such as game theory, basic psychology and logic. A precise definition of what 'optimization' really means in this contexts is imperative to the further usefulness or this endeavor. So here goes: According to me, any optimization problem that involves multiple intelligent entities ('intelligent': capable of making their own decisions with some idea of their consequences) can be treated in two fundamentally different ways. The first solution treats the problem from a single individual's point of view and aims at maximizing the benefit of one single entity (say one ant in an entire colony of ants). The other solution is the utilitarian approach of working out a scheme that results in the 'maximum benefit for the maximum number of people'. As we shall see, he two paths can lead to very different models and would dictate contrasting measures to participating entities. I should also make it clear that every participating entity in any social environment is assumed to be rational, meaning it is interested in maximizing it's own benefit. Also, 'benefit' for our purpose shall refer to some form of positive gratification, material or otherwise, that can ultimately be translated to happiness.
Okay, now that the boring groundwork on definitions and notations has been laid out, let me start by saying that I originally wrote this as a reference for myself, so I could look it up when I was faced with a certain social situation and maybe come up with an answer (Hmm.. since I'm using the word 'social' so often let me also define it (this is the last definition I promise. 'Social' for my purpose simply means anything to do with other individuals, either directly or indirectly.). I learnt along the way, that social interaction is a learning process, much like a neural network. A person's behavior in a give social setting is determined by the sum total of his/her experiences in the past and even though (s)he may not have experienced anything like the present scenario, a natural output produced to the offered stimulus, would be a determinate function of the past and not a random action. In what follows, I will try and elucidate the general concept of 'behavioral optimization for social interaction' through specific cases and examples.
P A R T - 1 .[Abstract]
This essay attempts to construct a logical and mathematical framework for modeling and studying social interaction patterns. It aims at allowing meaningful decision-making for the purpose of optimization of social participation. In this endeavor, it makes use of well known tools such as game theory, basic psychology and logic. A precise definition of what 'optimization' really means in this contexts is imperative to the further usefulness or this endeavor. So here goes: According to me, any optimization problem that involves multiple intelligent entities ('intelligent': capable of making their own decisions with some idea of their consequences) can be treated in two fundamentally different ways. The first solution treats the problem from a single individual's point of view and aims at maximizing the benefit of one single entity (say one ant in an entire colony of ants). The other solution is the utilitarian approach of working out a scheme that results in the 'maximum benefit for the maximum number of people'. As we shall see, he two paths can lead to very different models and would dictate contrasting measures to participating entities. I should also make it clear that every participating entity in any social environment is assumed to be rational, meaning it is interested in maximizing it's own benefit. Also, 'benefit' for our purpose shall refer to some form of positive gratification, material or otherwise, that can ultimately be translated to happiness.
Okay, now that the boring groundwork on definitions and notations has been laid out, let me start by saying that I originally wrote this as a reference for myself, so I could look it up when I was faced with a certain social situation and maybe come up with an answer (Hmm.. since I'm using the word 'social' so often let me also define it (this is the last definition I promise. 'Social' for my purpose simply means anything to do with other individuals, either directly or indirectly.). I learnt along the way, that social interaction is a learning process, much like a neural network. A person's behavior in a give social setting is determined by the sum total of his/her experiences in the past and even though (s)he may not have experienced anything like the present scenario, a natural output produced to the offered stimulus, would be a determinate function of the past and not a random action. In what follows, I will try and elucidate the general concept of 'behavioral optimization for social interaction' through specific cases and examples.
0 Comments:
Post a Comment
<< Home