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Basically, economic decision making is not restricted to mundane finance, the managing of money, but any decision that involves expected utility (some result with some degree of optimality.)

  • Can Machine Learning algorithms make economic decisions as well as or better than humans?

"Like humans" means understanding classes of objects and their interactions, including agents such as other humans.

At a fundamental level, there must be some physical representation of an object, leading to usage of an object, leading to management of resources that the objects constitute.

This may include ability to effectively handle semantic data (NLP) because mcuh of the relevant information is communicated in human languages.

DukeZhou
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user8426627
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2 Answers2

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Consider managing a memory structure as an economic function. (Where to put, and how to manage, the resources constituted by data.) This is something computers can do better and faster than any human. The reason is that the system in which the economic decisions are being made is fully defined.

Routing of packages is a similar, economic function that computers do much better than humans.

These functions haven't been handled by Machine Learning in the past, but, soon after the AlphaGo milestone, Google found an economic application for Machine Learning. Google's DeepMind trains AI to cut its energy bills by 40% (Wired)

So it's entirely context dependent.

As the model increases in complexity and nuanced, utility will be reduced. (In the former case it's a time and space issue related to computational complexity, and in the latter case, often a function of incomplete information or inability to define parameters.)

But as the sophistication of the machine learning algorithms increases, and the models continue to be refined, the algorithms will get better and better at managing intractability and incomplete information.

DukeZhou
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at this time, as open source - NOT.

i guess:

  • for a decision make we need a broad input layer/-s of data flows,
  • we need a 20 000-200 000 layers of neural networks or more complex and dynamic architectures
  • we need a deep research of date-time influence for historical data flow

what we have at this time:

  • only sensors - opencv and object recognition, nlp-tagging, data predicting

so, sensors isn't AI, sensors and machine learning is previous experience. it is not ready for the change analysis.

nexoma
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