Zuerst mal habe ich in der Sonntagszeitung einen netten Artikel über hunch.com gelesen. Hunch ist eine automatisierte Entscheidungsmaschine. Und so beschreibt sich Hunch selber:
In 10 questions or less, Hunch will offer you a great solution to your problem, concern or dilemma, on hundreds of topics. Hunch’s answers are based on the collective knowledge of the entire Hunch community, narrowed down to people like you, or just enough like you that you might be mistaken for each other in a dark room. Hunch is designed so that every time it’s used, it learns something new. That means Hunch’s hunches are always getting better.
Und so funktionierts:
At the core of Hunch is a question selection algorithm built by our small gaggle of MIT computer scientists with backgrounds in machine learning. The algorithm is always asking itself, “What can I ask you next which will lead to the best possible result for this decision?” The choice of which questions to ask and when to ask them will vary based on what you’ve already been asked (and how you’ve answered) so far, the same way that a human expert would adjust a line of questioning based on your responses. The idea is that if someone says they’re a vegetarian, you don’t want to then immediately ask them how they want their steak cooked.
In choosing what to ask you, Hunch’s question selection algorithm tries to do two things. First, it tries to find a question which will discriminate well among the remaining possible decision outcomes for you – thus filtering the remaining choices from “many” to “fewer”. Second, the algorithm looks for a question which can help optimize and rank the remaining decision results to present you with the ones you’ll like the most. It’s trying to ensure that you’ll like outcome #1 better than outcome #5.
Am Nachmittag haben mich meine Cousine und ihr Freund, beides Ökonomen, auf eine Fahrradtour zu meiner Grossmutter mitgenommen. Dabei haben sie mich noch auf ein paar Ideen gebracht. Anscheinend arbeitet die Behaviourial Finance mit Modellen, die ich “semantische Analyse” nennen würde. Sie kategorisieren Begriffe und ihre positiven oder negativen Konnotationen. Bei einem Text-Mining diverser Finanzmedien, werden die Texte auf diese Begriffe geprüft. Damit möchte man Hinweise auf den Zustand der Märkte gewinnen. Anscheinend werden solche Methoden vor allem bei Hedgefunds angewendet.
