Phil 10.9.17

7:00 – 9:30, 5:00 – 7:00 ASRC MKT

  • Writing up review. I also stumbled across a good book on Complex Systems in Finance and Economics that is tangentially related to the paper. They have a chart on page 764 that shows the development trajectories of the multiple threads in the related fields.
  • Radiolab did a revisit to the trolley problem with respect to self-driving cars. In the end discussion, they state that the problem is a small edge condition. I think under normal conditions that’s true. Under catastrophic conditions like a post earthquake evacuation, every trip could be the trolley problem. With TaaS, who gets picked up first? who has priority on the road? Pinged Radiolab about that. Curious if they will respond.
  • Good chat with Cindy. She found a bunch of stuff, including this part about moral dilemmas. We also started thinking about the chat game design. And we found her comments! Seems like WordPress isn’t alerting me when they get submitted.
  • Nvivo for Mac

2 thoughts on “Phil 10.9.17

  1. Cindy Flatley

    Over the weekend, talking about this work, someone mentioned ants/pheromones as a potential analog for trust in multi-agent information foraging. It is already being reviewed for artificial intelligence and robotics applications, so I thought you might find it interesting for your purposes, too. Here’s an article:
    A Pheromone-Based Utility Model for Collaborative Foraging
    Abstract: “Multi-agent research often borrows from biology, where
    remarkable examples of collective intelligence may be
    found. One interesting example is ant colonies’ use of
    pheromones as a joint communication mechanism. In
    this paper we propose two pheromone-based algorithms
    for artificial agent foraging, trail-creation, and other
    tasks. Whereas practically all previous work in this area
    has focused on biologically-plausible but ad-hoc single
    pheromone models, we have developed a formalism which
    uses multiple pheromones to guide cooperative tasks. This
    model bears some similarity to reinforcement learning.
    However, our model takes advantage of symmetries common
    to foraging environments which enables it to achieve
    much faster reward propagation than reinforcement learning
    does. Using this approach we demonstrate cooperative
    behaviors well beyond the previous ant-foraging work, including
    the ability to create optimal foraging paths in the
    presence of obstacles, to cope with dynamic environments,
    and to follow tours with multiple waypoints. We believe that
    this model may be used for more complex problems still.”

    1. Cindy Flatley

      Last week I did some initial forays into researching fashion trend movement data, designer vs. retail sales indications of transferring trends to the general public, etc. I did find that fashion forecasting is a robust industry, but I was unable to find much that I thought you’d find helpful in your work.

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