7:00 – 5:00 ASRC MKT
- Big thought for today.In a civilization context, the three phases of collective intelligence work like this. These phases relate to computational effort which is proportional to the number of dimensions that an individual has to consider in their existential calculus. The assumption is that lower computational effort is selected for at natural explore/exploit ratios.
- Exploration phase. Nomadic explorers are introduced to a new environment. Can be physical, informational, cognitive, etc. This phase has the highest dimensional processing required for the individual.
- Exploitation phase. Social patterns increase the hill climbing power of agents in the environment. This results in a sufficiently optimal access to resources. This employs lower dimensions to support consensus and polarization.
- Inertial phase. Social influence becomes dominant and environmental influence wains. Local diversity drops as similar agents cluster tightly together. Resources wane. This employs the most dimension reduction and the highest polarization, resulting in high implicit coordination.
- Collapse. Implied, since the Inertial phase is unsustainable. If the previous population produced explorers that found new, productive environments, the cycle can repeat elsewhere.
- Continuing BIC
- “We need to know, in detail, what deliberations are like that people engage in when they group-identify”. Also, agency transformation.
- Rules, norms and institutional erosion: Of non-compliance, enforcement and lack of rule of law
- What I am seeing right now in the US (a steady and slow erosion of democratic norms and a systematic violation of rules by the President Elect, in particular as though “they don’t apply to him“) is something that I’ve seen in other countries where I have studied formal and informal rules and institution building (and decay). This, in my view, is worrisome. If the US is going to want to continue having a functioning democracy where compliance with rules and norms is an expectation at the societal level, it’s going to have to do something major to stop this systematic rule violation.
- Evaluation of Interactive Machine Learning Systems
- The evaluation of interactive machine learning systems remains a difficult task. These systems learn from and adapt to the human, but at the same time, the human receives feedback and adapts to the system. Getting a clear understanding of these subtle mechanisms of co-operation and co-adaptation is challenging. In this chapter, we report on our experience in designing and evaluating various interactive machine learning applications from different domains. We argue for coupling two types of validation: algorithm-centered analysis, to study the computational behaviour of the system; and human-centered evaluation, to observe the utility and effectiveness of the application for end-users. We use a visual analytics application for guided search, built using an interactive evolutionary approach, as an exemplar of our work. We argue that human-centered design and evaluation complement algorithmic analysis, and can play an important role in addressing the “black-box” effect of machine learning. Finally, we discuss research opportunities that require human-computer interaction methodologies, in order to support both the visible and hidden roles that humans play in interactive machine learning.
- Jensen–Shannon divergence – I think I can use this to show the distance between a full coordination matrix and one that contains only the main diagonal.
- In probability theory and statistics, the Jensen–Shannon divergence is a method of measuring the similarity between two probability distributions. It is also known as information radius (IRad) or total divergence to the average. It is based on the Kullback–Leibler divergence, with some notable (and useful) differences, including that it is symmetric and it is always a finite value. The square root of the Jensen–Shannon divergence is a metric often referred to as Jensen-Shannon distance.
- Evolution of social behavior in finite populations: A payoff transformation in general n-player games and its implications
- The evolution of social behavior has been the focus of many theoretical investigations, which typically have assumed infinite populations and specific payoff structures. This paper explores the evolution of social behavior in a finite population using a general -player game. First, we classify social behaviors in a group of individuals based on their effects on the actor’s and the social partner’s payoffs, showing that in general such classification is possible only for a given composition of strategies in the group. Second, we introduce a novel transformation of payoffs in the general -player game to formulate explicitly the effects of a social behavior on the actor’s and the social partners’ payoffs. Third, using the transformed payoffs, we derive the conditions for a social behavior to be favored by natural selection in a well-mixed population and in the presence of multilevel selection.
- Got the data for the verdicts and live verdicts set up right, or at least closer:
- Booked a room for the CHIIR Hotel
- Got farther on UltimateAngular: