Tentative findings/implications for design

This page contains my evolving thinking on my dissertation


  • The overall model is conceptually a set of nodes connected by edges of varying stiffness, where the nodes have a velocity and orientation over an n-dimensional fitness landscape.
  • Stiff links makes the whole network move like a single agent. Links can be so slack that they do not affect agents. Links can also repel. The math that describes this is signed graph Laplacians. The links between agents are manifestations of what we would interpret as awareness and trust
  • Nodes have position, orientation, and velocity. They can adjust their heading and velocity based on neighboring agents. Currently, that’s based on physical distance (Social influence Horizon), but it should be based on the edges between the nodes. In normal physics, the equation that governs the interactions between the nodes is mass/spring/damper, with the influence of each link dictated by the value in the adjacency matrix.  In this case, we’re not dealing with physics, so the governing equations needs to be uncovered. My strawman is:
    • stiffness (k) = (C/Var(P) * C/Var(O) * Avg(V)/Var(V))/Sd, for some group g, over a period of time t, where
    • Var(x) is the variance
    • Avg(x) is the average
    • P is position
    • O is orientation
    • V is velocity
    • C is an unknown constant
    • Sd is Similarity Distance (Or social influence horizon). It’s the belief-space distance between agents
    • The node behaviors in the network should have (at least?) three phases: Nomad (low), Flocking(mid), and Stampede(high). The intuition here is that the higher the velocity, the lower the variance has to be in position and orientation to obtain the same level of cohesion. A high velocity, tightly clustered group is a stampede, where social influence overrides environmental awareness.
  • H1: Group Polarization (Stampede) is defined by a common position, orientation, and velocity (POV) through a navigable physical or cognitive space. The amount of group cohesion and identification is proportional to the amount of similarity along all three axis. Group membership reduces the amount of information that an individual agent has to process as it exchanges direct knowledge to social influence. At the limit of complete social influence, the group behaves like a single individual. Imagine a class taking an exam where all the students copy from each other.
  • H2: Group Behavior, a form of intelligent phase locking, emerges from mutual influence, based on link strength and density. Mutual influence is facilitated by Dimension Reduction: The lower the number of dimensions, the easier it is to produce a group.
  • H3: Group behavior has three distinct patterns: NomadicFlocking and Stampeding. These behaviors are dictated by the level of trust and awareness between individuals having similar LOVs
    • H3a: The trustworthiness of the underlying information space can be inferred from the group behaviors through belief space. All agents  seek out fitness peaks (reward gradients) and avoids valleys (risk gradients) within the space. (Risk = negative heading alignment, increase speed. Reward = positive heading alignment, decrease speed.)
      • Nomadic emphasizes environmental gradients as an individual or small group of agents. This supports the broadest awareness of the belief space, though it may be difficult to infer fitness peaks. Gradient discovery is  less influences by additional social effects,
      • Flocking behavior results from environmentally constrained social gradient seeking. For example, distance attenuates social influence. If an agent finds a risk or reward, that information cascades through the population as a function of the environmental constraints. (Note: In-group and out group could be manifestations of pure social gradient creation.)
      • Stampede emphasizes social gradients. This becomes easier as groups become larger and a strong ‘social reality’ occurs. When social influence is dominant at the expense of environmental awareness, a runaway stampede can occur. The beliefs and associated information that underlie a stampede can be inferred to be untrustworthy.
  • H4: Individual trajectories through these spaces, when combined with large numbers of other individual trajectories produce maps which reflect the dimensions that define the groups in that space.
  • These conclusions can be derived though
  • H5: These behavior patterns as manifested by humans are reflected in the great sociocultural user interfaces – Maps, Lists, and Stories


Some thoughts on alignment in belief space

The Great Socio-cultural Interfaces: Lists, Stories, and Maps

Some thoughts about awareness and trust

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 wanes. Local diversity drops as similar agents cluster tightly together. Accessible 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.
  • Hierarchical Control. This would be an alternative to inertia. If leader(s) are sufficiently successful then blind trust can make a lot of sense. It works for multicellular organisms, particularly those with a nervous system. Also armies. There needs to be some level of feedback from the somatic agents to the dictating agents to handle things like injury.