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Annotation 7707095862540

#abm #agent-based #machine-learning #model #priority
We demonstrated the advantages of this approach by applying it to reproduce the results of the prominent Sugarscape model. To show the flexibility of the framework, we then made slight changes to the modelled system by removing the competition between the agents. While a traditional approach to agent-based modelling would require a reformulation of the rules for agent behaviour, here the Neural Network is automati- cally retrained to accommodate the changes in the system and we naturally end up with realistic agent behaviour
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negative or neutral. Here, the Neural Network is not used as a form of optimization, but rather as a realistic depiction of a decision process, including the possibility of errors in judgement. <span>We demonstrated the advantages of this approach by applying it to reproduce the results of the prominent Sugarscape model. To show the flexibility of the framework, we then made slight changes to the modelled system by removing the competition between the agents. While a traditional approach to agent-based modelling would require a reformulation of the rules for agent behaviour, here the Neural Network is automati- cally retrained to accommodate the changes in the system and we naturally end up with realistic agent behaviour. We also explored the limits of the framework and found that the original approach fails, once system states that are relevant, if agents act to reach a goal, do not appear during an Ex

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Annotation 7707098483980

#ML_in_Action #learning #machine #software-engineering
I had some successes but many failures, and generally left a trail of unmaintainable code in my wake as I moved from job to job. It’s not something that I’m particularly proud of. I’ve been contacted by former colleagues, years after leaving a position, to have them tell me that my code is still running every day. When I’ve asked each one of them why, I’ve gotten the same demoralizing answer that has made me regret my implementations: “No one can figure it out to make changes to it, and it’s too important to turn off.”
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I had some successes but many failures, and generally left a trail of unmaintainable code in my wake as I moved from job to job. It’s not something that I’m particularly proud of. I’ve been contacted by former colleagues, years after leaving a position, to have them tell me that my code is still running every day. When I’ve asked each one of them why, I’ve gotten the same demoralizing answer that has made me regret my implementations: “No one can figure it out to make changes to it, and it’s too important to turn off.” I’ve been a bad data scientist. I’ve been an even worse ML engineer. It took me years to learn why that is. That stubbornness and resistance to solving problems in the simplest way crea

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