7:00 – 5:00 ASRC GOES

• Antonio has the TAAS paper
• Dissertation
  • Starting at the beginning. Paragraphs have been written!
• Demo at NOAA today
  • Change the timeseries filename to include date and time information – done
  • Demo went well. Bruce helped a lot, which means he’s on board with the concepts
• Thinking about maintaining anonymity on JuryRoom
  • Anonymity is the default
  • In the menu, there is something that says “You are posting as XX”, where XX is a 2-character label that is generated for every Jury.
  • In the same spot is a “Post as Yyyyy”, where Yyyyy is the login name. Clicking that will reverse the statements so that the bar reads “You are posting as Yyyyy”
  • If posting, all players get a color, rather than their Icon
  • In the database, the presence of a label is a flag for anonymous posting. If the user is presenting as themselves, then that string is NULL, so it’s an easy test.
• Social learning strategies regulate the wisdom and madness of interactive crowds
  • Why groups of individuals sometimes exhibit collective ‘wisdom’ and other times maladaptive ‘herding’ is an enduring conundrum. Here we show that this apparent conflict is regulated by the social learning strategies deployed. We examined the patterns of human social learning through an interactive online experiment with 699 participants, varying both task uncertainty and group size, then used hierarchical Bayesian model fitting to identify the individual learning strategies exhibited by participants. Challenging tasks elicit greater conformity among individuals, with rates of copying increasing with group size, leading to high probabilities of herding among large groups confronted with uncertainty. Conversely, the reduced social learning of small groups, and the greater probability that social information would be accurate for less-challenging tasks, generated ‘wisdom of the crowd’ effects in other circumstances. Our model-based approach provides evidence that the likelihood of collective intelligence versus herding can be predicted, resolving a long-standing puzzle in the literature.
• Locally noisy autonomous agents improve global human coordination in network experiments
  • Coordination in groups faces a sub-optimization problem^1,2,3,4,5,6 and theory suggests that some randomness may help to achieve global optima^7,8,9. Here we performed experiments involving a networked colour coordination game¹⁰ in which groups of humans interacted with autonomous software agents (known as bots). Subjects (n = 4,000) were embedded in networks (n = 230) of 20 nodes, to which we sometimes added 3 bots. The bots were programmed with varying levels of behavioural randomness and different geodesic locations. We show that bots acting with small levels of random noise and placed in central locations meaningfully improve the collective performance of human groups, accelerating the median solution time by 55.6%. This is especially the case when the coordination problem is hard. Behavioural randomness worked not only by making the task of humans to whom the bots were connected easier, but also by affecting the gameplay of the humans among themselves and hence creating further cascades of benefit in global coordination in these heterogeneous systems.