Posts Tagged ‘theory’

Everyday politics :)

Thursday, July 30th, 2009

Aristotle said that, by nature, we are all political animals.

When I wrote in the title “everyday politics” I was not thinking about “professional” politics.

I was simply stating: with the new technologies, that disrupt the usual communication organizations and control, everybody can do a political action. Everyday.

Read below to see how to begin now. Without a budget. Just a brain and a keyboard, and some experience to share.

Searching & Machine intelligence & Decisions

Tuesday, May 19th, 2009

How the addition of WolframAlpha as a search engine could complement Google services to create a new market.

Services? Access and structure knowledge. And a new form of knowledge management.

GMN2009: Playing

Tuesday, May 19th, 2009

Moving from designing the game to using the game implies adding a completely different set of skills.

You will need to learn how to manage the evolution of the dynamics between the players- be it a simple game, a real negotiation, the management of a major organizational change, a merger, or a political campaign.

Playing games implies being able to constantly feed back into your game the results of what you are doing.

From designing the game, we will move to evolution- something that is not necessarily following your strategy- but that you have to cope with.

This post is part of a series, first published in May 2009.

GMN2009: Games

Monday, May 18th, 2009

You want your model to work in reality, and therefore you have to assume that others have their own models.

It is a game. Like playing chess. Or the usual “prisoner’s dilemma”.

From models, we will move to the interaction between models- and between different decision paths within a model.

A down-to-earth introduction to the game theory.

This post is part of a series, first published in May 2009.

GMN2009: Reality

Friday, May 15th, 2009

When you build a model of reality, you try to reduce complexity.

Reducing complexity means making choices- and reducing the risk of something unexpected affecting the results of your model.

Actually, it means also reducing the number of parameters- and, therefore, making any evolution in your world more predictable.

But reality is not necessarily limited by your definition: and managing the reality within a model requires more that planning beforehand for what you know, in terms of activities or risks.

You have also to identify what is the “normal” way in which your model will react to unexpected changes in the “reality” surrounding your model.

This post is part of a series, first published in May 2009.