Phil 11.1.18

7:00 – 4:30 ASRC PhD

  • Quick thought. Stampedes may be recognized not just from low variance (density of connections), but also the speed that a new term moves into the lexicon (stiffness)
  • The Junk News Aggregator, the Visual Junk News Aggregator and the Top 10 Junk News Aggregator are research projects of the Computational Propaganda group (COMPROP) of the Oxford Internet Institute (OII)at the University of Oxford.These aggregators are intended as tools to help researchers, journalists, and the public see what English language junk news stories are being shared and engaged with on Facebook, ahead of the 2018 US midterm elections on November 6, 2018.The aggregators show junk news posts along with how many reactions they received, for all eight types of post reactions available on Facebook, namely: Likes, Comments, Shares, and the five emoji reactions: Love, Haha, Wow, Angry, and Sad.
  • Reading Charles Perrow’s Normal Accidents. Riveting. All about dense, tightly connected networks with hidden information
    • From The Montreal Review
      • Normal Accident drew attention to two different forms of organizational structure that Herbert Simon had pointed to years before, vertical integration, and what we now call modularity. Examining risky systems in the Accident book, I focused upon the unexpected interactions of different parts of the system that no designer could have expected and no operator comprehend or be able to interdict. Reading Charles Perrow’s Normal Accidents. Riveting. All about dense, tightly connected networks with hidden information
  • Building generators.
    • Need to change the “stepsize” in the Torrance generator to be variable – done. Here’s my little ode to The Shining:
      #confg: {"rows":100, "sequence_length":26, "step":26, "type":"words"}
      all work and no play makes jack a dull boy all work and no play makes jack a dull boy all work and no play makes 
      jack a dull boy all work and no play makes jack a dull boy all work and no play makes jack a dull boy all work 
      and no play makes jack a dull boy all work and no play makes jack a dull boy all work and no play makes jack a 
      dull boy all work and no play makes jack a dull boy all work and no play makes jack a dull boy all work and no 
      play makes jack a dull boy all work and no play makes jack a dull boy all work and no play makes jack a dull boy 
      all work and no play makes jack a dull boy all work and no play makes jack a dull boy all work and no play makes 
      jack a dull boy all work and no play makes jack a dull boy all work and no play makes jack a dull boy all work 
      and no play makes jack a dull boy all work and no play makes jack a dull boy all work and no play makes jack a 
      dull boy all work and no play makes jack a dull boy all work and no play makes jack a dull boy all work and no 
      play makes jack a dull boy all work and no play makes jack a dull boy all work and no play makes jack a dull boy 
      all work and no play makes jack a dull boy all work and no play makes jack a dull boy all work and no play makes 
      jack a dull boy all work and no play makes jack a dull boy all work and no play makes jack a dull boy all work 
      and no play makes jack a dull boy all work and no play makes jack a dull boy all work and no play makes jack a 
      
    • Need to be able to turn out a numeric equivalent. Done with floating point. This:
      #confg: {"function":math.sin(xx)*math.sin(xx/2.0)*math.cos(xx/4.0), "rows":100, "sequence_length":20, "step":1, "delta":0.4, "type":"floating_point"}
      0.0,0.07697897630719268,0.27378318599563484,0.5027638400821064,0.6604469814238397,0.6714800165989514,0.519596709539434,0.2524851001382131,-0.04065231596017931,-0.2678812526747579,-0.37181365763470914,-0.34898182120310306,-0.24382057359778858,-0.12182487479311599,-0.035942415169752356,-0.0027892469005274916,0.00019865778200507415,0.016268713740310237,0.07979661440830532,0.19146155036709192,
      0.07697897630719312,0.2737831859956355,0.5027638400821071,0.6604469814238401,0.6714800165989512,0.5195967095394334,0.2524851001382121,-0.04065231596018022,-0.26788125267475843,-0.37181365763470925,-0.3489818212031028,-0.24382057359778805,-0.12182487479311552,-0.0359424151697521,-0.0027892469005274395,0.0001986577820050832,0.016268713740310397,0.07979661440830574,0.19146155036709248,0.31158944024296154,
      0.2737831859956368,0.502763840082108,0.6604469814238405,0.6714800165989508,0.5195967095394324,0.25248510013821085,-0.04065231596018143,-0.2678812526747592,-0.37181365763470936,-0.34898182120310245,-0.24382057359778747,-0.12182487479311502,-0.03594241516975184,-0.002789246900527388,0.00019865778200509222,0.01626871374031056,0.07979661440830614,0.191461550367093,0.311589440242962,0.3760334615921674,
      0.5027638400821092,0.6604469814238411,0.6714800165989505,0.5195967095394312,0.25248510013820913,-0.040652315960182955,-0.26788125267476015,-0.37181365763470964,-0.348981821203102,-0.24382057359778667,-0.12182487479311428,-0.03594241516975145,-0.0027892469005273107,0.00019865778200510578,0.016268713740310803,0.07979661440830675,0.1914615503670939,0.3115894402429629,0.3760334615921675,0.3275646734005755,
      0.660446981423842,0.6714800165989498,0.5195967095394289,0.2524851001382062,-0.04065231596018568,-0.2678812526747618,-0.37181365763471,-0.34898182120310123,-0.24382057359778553,-0.1218248747931133,-0.03594241516975093,-0.0027892469005272066,0.00019865778200512388,0.016268713740311122,0.07979661440830756,0.19146155036709495,0.31158944024296387,0.3760334615921676,0.3275646734005745,0.1475692800414062,
      0.671480016598949,0.5195967095394267,0.25248510013820324,-0.04065231596018842,-0.2678812526747636,-0.3718136576347104,-0.34898182120310045,-0.24382057359778414,-0.12182487479311209,-0.03594241516975028,-0.002789246900527077,0.0001986577820051465,0.016268713740311528,0.07979661440830856,0.19146155036709636,0.3115894402429648,0.37603346159216783,0.32756467340057344,0.1475692800414041,-0.12805444308254293,
      0.5195967095394245,0.2524851001382003,-0.04065231596019116,-0.2678812526747653,-0.3718136576347107,-0.3489818212030998,-0.24382057359778303,-0.12182487479311109,-0.03594241516974975,-0.0027892469005269733,0.00019865778200516457,0.016268713740311847,0.07979661440830936,0.19146155036709747,0.3115894402429657,0.37603346159216794,0.32756467340057244,0.147569280041402,-0.1280544430825456,-0.41793663502550105,
      0.2524851001381973,-0.04065231596019389,-0.26788125267476703,-0.3718136576347111,-0.3489818212030989,-0.2438205735977817,-0.12182487479310988,-0.0359424151697491,-0.002789246900526843,0.00019865778200518717,0.01626871374031225,0.07979661440831039,0.1914615503670989,0.3115894402429671,0.3760334615921681,0.3275646734005709,0.14756928004139883,-0.1280544430825496,-0.41793663502550454,-0.6266831461371138,
      
    • Gives this: Waves
    • Need to write a generator that reads in text (words and characters) and produces data tables with stepsizes
    • Need to write a generator that takes an equation as a waveform
  • USPTO Meeting. Use NN to produce multiple centrality / laplacians that user interact with
  • Working on my 810 tasks
    • Potentially useful for mapmaking: Learning the Preferences of Ignorant, Inconsistent Agents
      • An important use of machine learning is to learn what people value. What posts or photos should a user be shown? Which jobs or activities would a person find rewarding? In each case, observations of people’s past choices can inform our inferences about their likes and preferences. If we assume that choices are approximately optimal according to some utility function, we can treat preference inference as Bayesian inverse planning. That is, given a prior on utility functions and some observed choices, we invert an optimal decision-making process to infer a posterior distribution on utility functions. However, people often deviate from approximate optimality. They have false beliefs, their planning is sub-optimal, and their choices may be temporally inconsistent due to hyperbolic discounting and other biases. We demonstrate how to incorporate these deviations into algorithms for preference inference by constructing generative models of planning for agents who are subject to false beliefs and time inconsistency. We explore the inferences these models make about preferences, beliefs, and biases. We present a behavioral experiment in which human subjects perform preference inference given the same observations of choices as our model. Results show that human subjects (like our model) explain choices in terms of systematic deviations from optimal behavior and suggest that they take such deviations into account when inferring preferences.
    • An Overview of the Schwartz Theory of Basic Values (Added to normative map making) Schwartz
      • This article presents an overview of the Schwartz theory of basic human values. It discusses the nature of values and spells out the features that are common to all values and what distinguishes one value from another. The theory identifies ten basic personal values that are recognized across cultures and explains where they come from. At the heart of the theory is the idea that values form a circular structure that reflects the motivations each value expresses. This circular structure, that captures the conflicts and compatibility among the ten values is apparently culturally universal. The article elucidates the psychological principles that give rise to it. Next, it presents the two major methods developed to measure the basic values, the Schwartz Value Survey and the Portrait Values Questionnaire. Findings from 82 countries, based on these and other methods, provide evidence for the validity of the theory across cultures. The findings reveal substantial differences in the value priorities of individuals. Surprisingly, however, the average value priorities of most societal groups exhibit a similar hierarchical order whose existence the article explains. The last section of the article clarifies how values differ from other concepts used to explain behavior—attitudes, beliefs, norms, and traits.

Leave a Reply

Fill in your details below or click an icon to log in:

WordPress.com Logo

You are commenting using your WordPress.com account. Log Out /  Change )

Google+ photo

You are commenting using your Google+ account. Log Out /  Change )

Twitter picture

You are commenting using your Twitter account. Log Out /  Change )

Facebook photo

You are commenting using your Facebook account. Log Out /  Change )

Connecting to %s

This site uses Akismet to reduce spam. Learn how your comment data is processed.