Phil 4.19.18

8:00 – ASRC MKT/BD

    • Good discussion with Aaron about the agents navigating embedding space. This would be a great example of creating “more realistic” data from simulation that bridges the gap between simulation and human data. This becomes the basis for work producing text for inputs such as DHS input streams.
      • Get the embedding space from the Jack London corpora (crawl here)
      • Train a classifier that recognizes JL using the embedding vectors instead of the words. This allows for contextual closeness. Additionally, it might allow a corpus to be trained “at once” as a pattern in the embedding space using CNNs.
      • Train an NN(what type?) to produce sentences that contain words sent by agents that fool the classifier
      • Record the sentences as the trajectories
      • Reconstruct trajectories from the sentences and compare to the input
      • Some thoughts WRT generating Twitter data
        • Closely aligned agents can retweet (alignment measure?)
        • Less closely aligned agents can mention/respond, and also add their tweet
    • Handed off the proposal to Red Team. Still need to rework the Exec Summary. Nope. Doesn’t matter that the current exec summary does not comply with the requirements.
    • A dog with high social influence creates an adorable stampede:
    • Using Machine Learning to Replicate Chaotic Attractors and Calculate Lyapunov Exponents from Data
      • This is a paper that describes how ML can be used to predict the behavior of chaotic systems. An implication is that this technique could be used for early classification of nomadic/flocking/stampede behavior
    • Visualizing a Thinker’s Life
      • This paper presents a visualization framework that aids readers in understanding and analyzing the contents of medium-sized text collections that are typical for the opus of a single or few authors.We contribute several document-based visualization techniques to facilitate the exploration of the work of the German author Bazon Brock by depicting various aspects of its texts, such as the TextGenetics that shows the structure of the collection along with its chronology. The ConceptCircuit augments the TextGenetics with entities – persons and locations that were crucial to his work. All visualizations are sensitive to a wildcard-based phrase search that allows complex requests towards the author’s work. Further development, as well as expert reviews and discussions with the author Bazon Brock, focused on the assessment and comparison of visualizations based on automatic topic extraction against ones that are based on expert knowledge.

 

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Phil 3.2.18

7:00 – 5:00 ASRC MKT

  • Got Wayne’s comments. Will integrate and see if EasyChair will take it
  • Work on ONR WhitePaper
  • Twitter proposal?
  • Society for Personality and Social Psychology
    • The mission of SPSP is to advance the scienceteaching, and application of social and personality psychology. SPSP members aspire to understand individuals in their social contexts for the benefit of all people.
    • Social psychology is the scientific study of how people’s thoughts, feelings, and behaviors are influenced by the actual, imagined, or implied presence of others.
  • Rebecca Hofstein Grady
    • I am interested in the ways that bias and motivation can affect our reasoning and memory to influence the judgments and decisions that we make.  In particular, I am currently studying how these biases apply to real-world situations, such as political conflicts, hiring decisions, and legal decision-making.  I explore not only how biases affect decision-making but what people think about their own biases and the best ways to help correct them.
    • Data from a pre-publication independent replication initiative examining ten moral judgement effects

Phil 2.19.18

7:30 – 4:30 ASRC MKT

  • Back to BIC.
    • BIC_102 (page 102)
    • BIC107 (pg 107)
    • BIC107b (pg 107)
    • Sociality: Coordinating bodies, minds and groups
      • Human interaction, as opposed to aggregation, occurs in face-to-face groups. “Sociality theory” proposes that such groups have a nested, hierarchical structure, consisting of a few basic variations, or “core configurations.” These function in the coordination of human behavior, and are repeatedly assembled, generation to generation, in human ontogeny, and in daily life. If face-to-face groups are “the mind’s natural environment,” then we should expect human mental systems to correlate with core configurations. Features of groups that recur across generations could provide a descriptive paradigm for testable and non-intuitive evolutionary hypotheses about social and cognitive processes. This target article sketches three major topics in sociality theory, roughly corresponding to the interests of biologists, psychologists, and social scientists. These are (1) a multiple levels-of-selection view of Darwinism, part group selectionism, part developmental systems theory; (2) structural and psychological features of repeatedly assembled, concretely situated face-to-face coordination; and (3) superordinate, “unsituated” coordination at the level of large-scale societies. Sociality theory predicts a tension, perhaps unresolvable, between the social construction of knowledge, which facilitates coordination within groups, and the negotiation of the habitat, which requires some correspondence with contingencies in specific situations. This tension is relevant to ongoing debates about scientific realism, constructivism, and relativism in the philosophy and sociology of knowledge.
        • These definitions seem to span atomic (mother/child, etc), small group (situated, environmental), and societal (unsituated, normative)
      • Coordination occurs to the extent that knowledge and practice domains overlap or are complementary. I suggest that values serve as a medium. Humans live in a value-saturated environment; values are known from interactions with people, natural objects, and artifacts
        • Dimension reduction
  •  I’m starting to think that agents as gradient descent machines within networks is something to look for:
    • Individual Strategy Update and Emergence of Cooperation in Social Networks
      • In this article, we critically study whether social networks can explain the emergence of cooperative behavior. We carry out an extensive simulation program in which we study the most representative social dilemmas. For the Prisoner’s Dilemma, it turns out that the emergence of cooperation is dependent on the microdynamics. On the other hand, network clustering mostly facilitates global cooperation in the Stag Hunt game, whereas degree heterogeneity promotes cooperation in Snowdrift dilemmas. Thus, social networks do not promote cooperation in general, because the macro-outcome is not robust under change of dynamics. Therefore, having specific applications of interest in mind is crucial to include the appropriate microdetails in a good model.
    • Alex Peysakhovich and Adam Lerer
      • Prosocial learning agents solve generalized Stag Hunts better than selfish ones
        • Deep reinforcement learning has become an important paradigm for constructing agents that can enter complex multi-agent situations and improve their policies through experience. One commonly used technique is reactive training – applying standard RL methods while treating other agents as a part of the learner’s environment. It is known that in general-sum games reactive training can lead groups of agents to converge to inefficient outcomes. We focus on one such class of environments: Stag Hunt games. Here agents either choose a risky cooperative policy (which leads to high payoffs if both choose it but low payoffs to an agent who attempts it alone) or a safe one (which leads to a safe payoff no matter what). We ask how we can change the learning rule of a single agent to improve its outcomes in Stag Hunts that include other reactive learners. We extend existing work on reward-shaping in multi-agent reinforcement learning and show that that making a single agent prosocial, that is, making them care about the rewards of their partners can increase the probability that groups converge to good outcomes. Thus, even if we control a single agent in a group making that agent prosocial can increase our agent’s long-run payoff. We show experimentally that this result carries over to a variety of more complex environments with Stag Hunt-like dynamics including ones where agents must learn from raw input pixels.
      • The Good, the Bad, and the Unflinchingly Selfish: Cooperative Decision-Making Can Be Predicted with High Accuracy Using Only Three Behavioral Types
        • The human willingness to pay costs to benefit anonymous others is often explained by social preferences: rather than only valuing their own material payoff, people also care in some fashion about the outcomes of others. But how successful is this concept of outcome-based social preferences for actually predicting out-of-sample behavior? We investigate this question by having 1067 human subjects each make 20 cooperation decisions, and using machine learning to predict their last 5 choices based on their first 15. We find that decisions can be predicted with high accuracy by models that include outcome-based features and allow for heterogeneity across individuals in baseline cooperativeness and the weights placed on the outcome-based features (AUC=0.89). It is not necessary, however, to have a fully heterogeneous model — excellent predictive power (AUC=0.88) is achieved by a model that allows three different sets of baseline cooperativeness and feature weights (i.e. three behavioral types), defined based on the participant’s cooperation frequency in the 15 training trials: those who cooperated at least half the time, those who cooperated less than half the time, and those who never cooperated. Finally, we provide evidence that this inclination to cooperate cannot be well proxied by other personality/morality survey measures or demographics, and thus is a natural kind (or “cooperative phenotype”)
        • “least”, “intermediate” and “most” cooperative. Doesn’t give percentages, though it says that 17.8% were cooperative?

         

  • Talk Susan Gregurick (susan.gregurick@nih.gov)
    • All of Us research program
    • Opiod epidemic – trajectory modeling?
    • PZM21 computational drug
    • Develop advanced software and tools. Specialized generalizable and accessible tools for biomedicing (finding stream). Includes mobile, data indexing, etc.
    • NIH Data Fellows? Postdocs to senior industry
    • T32 funding? Mike Summers at UMBC
    • ncbi-hackathons.github.io (look for data?
    • Primary supporter for machine learning is NIMH (imaging), then NIGNS, and NCI Team science (Multi-PI) is a developing thing
    • $400m in computing enabled interactions (human in the loop decision tools. Research Browser?
    • Big Data to Knowledge Initiative (BD2K) datascience.nih.gov/bd2k
    • Interagency Modeling and Analysis Group (IMAG) imagewiki,nibib.nih.gov
    • funding: bisti.nih.gov
    • NIH RePorter projectreporter.nih.gov Check out matchmaker. What’s the ranking algorithm?
    • NIDDK predictive analytics for budgeting <- A2P-ish?
    • Most of thi srequires preliminary data and papers to be considered for funding. There is one opportunity for getting funding to get preliminary data. Need to get more specific infor here.
    • Each SRO normalizes grade as a percentile, not the score, since some places inflate, and others are hard.
    • Richard Aargon at NIGMS
    • Office of behavioral and social science – NIH center Francis Collins. Also agent-based simulation
    • Really wants a Research Browser to go through proposals
  • Fika – study design
    • IRB – you can email and chat with the board if you have a tricky study

Phil 2.13.18

7:00 – 4:00 ASRC MKT

  • UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction
    • UMAP (Uniform Manifold Approximation and Projection) is a novel manifold learning technique for dimension reduction. UMAP is constructed from a theoretical framework based in Riemannian geometry and algebraic topology. The result is a practical scalable algorithm that applies to real world data. The UMAP algorithm is competitive with t-SNE for visualization quality, and arguably preserves more of the global structure with superior run time performance. Furthermore, UMAP as described has no computational restrictions on embedding dimension, making it viable as a general purpose dimension reduction technique for machine learning.
  • How Prevalent are Filter Bubbles and Echo Chambers on Social Media? Not as Much as Conventional Wisdom Has It
    • Yet, as Rasmus points out, conventional wisdom seems to be stuck with the idea that social media constitute filter bubbles and echo chambers, where most people only, or mostly, see political content they already agree with. It is definitely true that there is a lot of easily accessible, clearly identifiable, highly partisan content on social media. It is also true that, to some extent, social media users can make choices as to which sources they follow and engage with. Whether people use these choice affordances solely to flock to content reinforcing their political preferences and prejudices, filtering out or avoiding content that espouses other viewpoints, is, however, an empirical question—not a destiny inscribed in the way social media and their algorithms function.
  • He Predicted The 2016 Fake News Crisis. Now He’s Worried About An Information Apocalypse.
    • That future, according to Ovadya, will arrive with a slew of slick, easy-to-use, and eventually seamless technological tools for manipulating perception and falsifying reality, for which terms have already been coined — “reality apathy,” “automated laser phishing,” and “human puppets.”
  • Finish first pass at DC slides – done!
  • Begin trimming paper – good progress.
  • Add a slider that lets the user interactively move a token along the selected trajectory path – done. Yes, it looks like a golf ball on a tee… Capture
  • Sprint planning

Phil 2.12.18

7:00 – 4:00 ASRC MKT

  • The social structural foundations of adaptation and transformation in social–ecological systems
    • Social networks are frequently cited as vital for facilitating successful adaptation and transformation in linked social–ecological systems to overcome pressing resource management challenges. Yet confusion remains over the precise nature of adaptation vs. transformation and the specific social network structures that facilitate these processes. Here, we adopt a network perspective to theorize a continuum of structural capacities in social–ecological systems that set the stage for effective adaptation and transformation. We begin by drawing on the resilience literature and the multilayered action situation to link processes of change in social–ecological systems to decision making across multiple layers of rules underpinning societal organization. We then present a framework that hypothesizes seven specific social–ecological network configurations that lay the structural foundation necessary for facilitating adaptation and transformation, given the type and magnitude of human action required. A key contribution of the framework is explicit consideration of how social networks relate to ecological structures and the particular environmental problem at hand. Of the seven configurations identified, three are linked to capacities conducive to adaptation and three to transformation, and one is hypothesized to be important for facilitating both processes.
  • Starting to trim paper down to three pages
  • Starting on CHIIR slide stack – Still need to add future work
  • Springt Review
  • Rwanda radio transcripts
    • From October 1993 to late 1994, RTLM was used by Hutu leaders to advance an extremist Hutu message and anti-Tutsi disinformation, spreading fear of a Tutsi genocide against Hutu, identifying specific Tutsi targets or areas where they could be found, and encouraging the progress of the genocide. In April 1994, Radio Rwanda began to advance a similar message, speaking for the national authorities, issuing directives on how and where to kill Tutsis, and congratulating those who had already taken part.
  • Fika
    • Set up Fika Writing group that will meet Wednesdays at 4:00. We’ll see how that goes.

Phil 2.7/18

7:30 – 5:30 ASRC MKT

  • Freezing rain and general ick, so I’m working from home. Thus leading to the inevitable updating of IntelliJ
  • Working on the 3D mapping app.
    • Reading in single spreadsheet with nomad graph info
    • Building a NodeInfo inner class to keep the nomad positions for the other populations
    • Working! 2018-02-07
    • Better: 2018-02-07 (2)
    • Resisting the urge to code more and getting back to the extended abstract. I also need to add a legend to the above pix.
  • Back to extended abstract
    • Added results and future work section
    • got all the pictures in
    • Currently at 3 pages plus. Not horrible.
  • Demographics and Dynamics of Mechanical Turk Workers
    • There are about 100K-200K unique workers on Amazon. On average, there are 2K-5K workers active on Amazon at any given time, which is equivalent to having 10K-25K full-time employees. On average, 50% of the worker population changes within 12-18 months. Workers exhibit widely different patterns of activity, with most workers being active only occasionally, and few workers being very active. Combining our results with the results from Hara et al, we see that MTurk has a yearly transaction volume of a few hundreds of millions of dollars.

Phil 2.6.18

7:30 – 5:00 ASRC MKT

  • Took four much needed days off on Sanibel island. Forgot to pack some things? Need to call the hotel at (239) 215-3401
  • Starting CI 2018 abstract. And oddly, the abstract isn’t showing??? Sent a note to the conference chair. IN the meantime, I have a subsection for the abstract. It appears to be acmlarge for the most part, so maybe use that????
  • Was going to get back to Angular, but stuck with 404s on CRUD operations: 404
  • Working on the 3D map application. Decided to go with JavaFX and their 3d implementation. It’s going quickly. MapApp1
  • I’ve also gotten the graph generator creating spreadsheets that the map app can read in. So the next job will be to wire everything together, where the position information is based off the nomad trajectories, with the size and visitor (height) data being overlayed with the different colors.

Phil 1.16.2018

ASRC MKT 7:00 – 4:30

  • Tit for tat in heterogeneous populations
    • The “iterated prisoner’s dilemma” is now the orthodox paradigm for the evolution of cooperation among selfish individuals. This viewpoint is strongly supported by Axelrod’s computer tournaments, where ‘tit for tat’ (TFT) finished first. This has stimulated interest in the role of reciprocity in biological societies. Most theoretical investigations, however, assumed homogeneous populations (the setting for evolutionary stable strategies) and programs immune to errors. Here we try to come closer to the biological situation by following a program that takes stochasticities into account and investigates representative samples. We find that a small fraction of TFT players is essential for the emergence of reciprocation in a heterogeneous population, but only paves the way for a more generous strategy. TFT is the pivot, rather than the aim, of an evolution towards cooperation.
    • It’s a Nature Note, so a quick read. In this case, the transition is from AllD->TFT->GTFT, where evolution stops.
  • A strategy of win-stay, lose-shift that outperforms tit-for-tat in the Prisoner’s Dilemma game
    • The Prisoner’s Dilemma is the leading metaphor for the evolution of cooperative behaviour in populations of selfish agents, especially since the well-known computer tournaments of Axelrod and their application to biological communities. In Axelrod’s simulations, the simple strategy tit-for-tat did outstandingly well and subsequently became the major paradigm for reciprocal altruism. Here we present extended evolutionary simulations of heterogeneous ensembles of probabilistic strategies including mutation and selection, and report the unexpected success of another protagonist: Pavlov. This strategy is as simple as tit-for-tat and embodies the fundamental behavioural mechanism win-stay, lose-shift, which seems to be a widespread rule. Pavlov’s success is based on two important advantages over tit-for-tat: it can correct occasional mistakes and exploit unconditional cooperators. This second feature prevents Pavlov populations from being undermined by unconditional cooperators, which in turn invite defectors. Pavlov seems to be more robust than tit-for-tat, suggesting that cooperative behaviour in natural situations may often be based on win-stay, lose-shift.
    • win-stay = exploit, lose-shift = explore
  • Five rules for the evolution of cooperation
    • Cooperation is needed for evolution to construct new levels of organization. The emergence of genomes, cells, multi-cellular organisms, social insects and human society are all based on cooperation. Cooperation means that selfish replicators forgo some of their reproductive potential to help one another. But natural selection implies competition and therefore opposes cooperation unless a specific mechanism is at work. Here I discuss five mechanisms for the evolution of cooperation: kin selection, direct reciprocity, indirect reciprocity, network reciprocity and group selection. For each mechanism, a simple rule is derived which specifies whether natural selection can lead to cooperation.
  • Added a paragraph to the previous work section to include Tit-for-Tat and Milti-armed Bandit previous work.
  • Worked with Aaron on setting up sprint goals

Phil 1.13.18

I think that burst-coast may be another one of those general patterns in collective intelligence

  • Disentangling and modeling interactions in fish with burst-and-coast swimming reveal distinct alignment and attraction behaviors
    • The development of tracking methods for automatically quantifying individual behavior and social interactions in animal groups has open up new perspectives for building quantitative and predictive models of collective behavior. In this work, we combine extensive data analyses with a modeling approach to measure, disentangle, and reconstruct the actual functional form of interactions involved in the coordination of swimming in Rummy-nose tetra (Hemigrammus rhodostomus). This species of fish performs burst-and-coast swimming behavior that consists of sudden heading changes combined with brief accelerations followed by quasi-passive, straight decelerations. We quantify the spontaneous stochastic behavior of a fish and the interactions that govern wall avoidance and the reaction to a neighboring fish, the latter by exploiting general symmetry constraints for the interactions. In contrast with previous experimental works, we find that both attraction and alignment behaviors control the reaction of fish to a neighbor. We then exploit these results to build a model of spontaneous burst-and-coast swimming and interactions of fish, with all parameters being estimated or directly measured from experiments. This model quantitatively reproduces the key features of the motion and spatial distributions observed in experiments with a single fish and with two fish. This demonstrates the power of our method that exploits large amounts of data for disentangling and fully characterizing the interactions that govern collective behaviors in animals groups.

Phil 1.5.17

7:00 – 3:30 ASRC MKT

  • Saw the new Star Wars film. That must be the most painful franchise to direct “Here’s an unlimited amount of money. You have unlimited freedom in these areas over here, and this giant pile is canon, that you  must adhere to…”
  • Wikipedia page view tool
  • My keyboard has died. Waiting on the new one and using the laptop in the interim. It’s not quite worth setting up the dual screen display. Might go for the mouse though. On a side note, the keyboard on my Lenovo Twist is quite nice.
  • More tweaking of the paper. Finished methods, on to results
  •  Here’s some evidence that we have mapping structures in our brain: Hippocampal Remapping and Its Entorhinal Origin
      • The activity of hippocampal cell ensembles is an accurate predictor of the position of an animal in its surrounding space. One key property of hippocampal cell ensembles is their ability to change in response to alterations in the surrounding environment, a phenomenon called remapping. In this review article, we present evidence for the distinct types of hippocampal remapping. The progressive divergence over time of cell ensembles active in different environments and the transition dynamics between pre-established maps are discussed. Finally, we review recent work demonstrating that hippocampal remapping can be triggered by neurons located in the entorhinal cortex.

     

  • Added a little to the database section, but spent most of the afternoon updating TF and trying it out on examples

Phil 1.4.17

7:00 – 3:00 ASRC MKT

  • Confidence modulates exploration and exploitation in value-based learning
    • Uncertainty is ubiquitous in cognitive processing, which is why agents require a precise handle on how to deal with the noise inherent in their mental operations. Previous research suggests that people possess a remarkable ability to track and report uncertainty, often in the form of confidence judgments. Here, we argue that humans use uncertainty inherent in their representations of value beliefs to arbitrate between exploration and exploitation. Such uncertainty is reflected in explicit confidence judgments. Using a novel variant of a multi-armed bandit paradigm, we studied how beliefs were formed and how uncertainty in the encoding of these value beliefs (belief confidence) evolved over time. We found that people used uncertainty to arbitrate between exploration and exploitation, reflected in a higher tendency towards exploration when their confidence in their value representations was low. We furthermore found that value uncertainty can be linked to frameworks of metacognition in decision making in two ways. First, belief confidence drives decision confidence — that is people’s evaluation of their own choices. Second, individuals with higher metacognitive insight into their choices were also better at tracing the uncertainty in their environment. Together, these findings argue that such uncertainty representations play a key role in the context of cognitive control.

  • Artificial Intelligence, AI in 2018 and beyond
    • Eugenio Culurciello
    • These are my opinions on where deep neural network and machine learning is headed in the larger field of artificial intelligence, and how we can get more and more sophisticated machines that can help us in our daily routines. Please note that these are not predictions of forecasts, but more a detailed analysis of the trajectory of the fields, the trends and the technical needs we have to achieve useful artificial intelligence. Not all machine learning is targeting artificial intelligences, and there are low-hanging fruits, which we will examine here also.
  • Synthetic Experiences: How Popular Culture Matters for Images of International Relations
    • Many researchers assert that popular culture warrants greater attention from international relations scholars. Yet work regarding the effects of popular culture on international relations has so far had a marginal impact. We believe that this gap leads mainstream scholars both to exaggerate the influence of canonical academic sources and to ignore the potentially great influence of popular culture on mass and elite audiences. Drawing on work from other disciplines, including cognitive science and psychology, we propose a theory of how fictional narratives can influence real actors’ behavior. As people read, watch, or otherwise consume fictional narratives, they process those stories as if they were actually witnessing the phenomena those narratives describe, even if those events may be unlikely or impossible. These “synthetic experiences” can change beliefs, reinforce preexisting views, or even displace knowledge gained from other sources for elites as well as mass audiences. Because ideas condition how agents act, we argue that international relations theorists should take seriously how popular culture propagates and shapes ideas about world politics. We demonstrate the plausibility of our theory by examining the influence of the US novelist Tom Clancy on issues such as US relations with the Soviet Union and 9/11.
  • Continuing with paper tweaking. Added T’s comments, and finished Methods.

Phil 12.28.12

8:30 – 4:30 ASRC MKT

  • Still sick. Nearing bronchitis?
  • Confessions of a Digital Nazi Hunter
  • Phenotyping of Clinical Time Series with LSTM Recurrent Neural Networks
    • We present a novel application of LSTM recurrent neural networks to multi label classification of diagnoses given variable-length time series of clinical measurements. Our method outperforms a strong baseline on a variety of metrics.
    • Scholar Cited by
      • Mapping Patient Trajectories using Longitudinal Extraction and Deep Learning in the MIMIC-III Critical Care Database
        • Electronic Health Records (EHRs) contain a wealth of patient data useful to biomedical researchers. At present, both the extraction of data and methods for analyses are frequently designed to work with a single snapshot of a patient’s record. Health care providers often perform and record actions in small batches over time. By extracting these care events, a sequence can be formed providing a trajectory for a patient’s interactions with the health care system. These care events also offer a basic heuristic for the level of attention a patient receives from health care providers. We show that is possible to learn meaningful embeddings from these care events using two deep learning techniques, unsupervised autoencoders and long short-term memory networks. We compare these methods to traditional machine learning methods which require a point in time snapshot to be extracted from an EHR.
  • Continuing on white paper
  • Moved the Flocking and Herding paper over to the WSC17 format for editing. Will need to move to the WSC18 format when that becomes available

Phil 12.27.17

8:00 – 4:00 ASRC MKT

  • Granted permission for the CHIIR18 DC.
  • Continuing on white paper. And we’ll see what Aaron has to say about the stampede paper today?
  • It occurs to be that it could make sense to read the trajectories in using the ARFF format. Looks straightforward, though I’d have to output each agent on an axis-by-axis basis. That would in turn mean that we’d have to save each ParticleStatement and save it out .
  • A new optimizer using particle swarm theory (1995)
    • The optimization of nonlinear functions using particle swarm methodology is described. Implementations of two paradigms are discussed and compared, including a recently developed locally oriented paradigm. Benchmark testing of both paradigms is described, and applications, including neural network training and robot task learning, are proposed. Relationships between particle swarm optimization and both artificial life and evolutionary computation are reviewed.
    • Cited by 12155

Phil 12.21.17

7:00 – 4:00 ASRC MKT

  • And now the days start to get longer!
  • Working on flocking and herding paper. Adding in the adversarial herding parts. Spent a lot of time working on getting a chart that tells the herding story. I’m somewhat ok with this: HerdingImpact
  • Some work on plotting norms using legal documents: Inferring Mechanisms for Global Constitutional Progress
    • Constitutions help define domestic political orders, but are known to be influenced by two international mechanisms: one that reflects global temporal trends in legal development, and another that reflects international network dynamics such as shared colonial history. We introduce the provision space; the growing set of all legal provisions existing in the world’s constitutions over time. Through this we uncover a third mechanism influencing constitutional change: hierarchical dependencies between legal provisions, under which the adoption of essential, fundamental provisions precedes more advanced provisions. This third mechanism appears to play an especially important role in the emergence of new political rights, and may therefore provide a useful roadmap for advocates of those rights. We further characterise each legal provision in terms of the strength of these mechanisms.
    • provisionSpace
  • A Lively Discussion, Even for KSJ: Edmond Awad on His ‘Moral Machine’
    • To collect vast amounts of data on human perspectives about such decisions, Awad and his team launched the Moral Machine website, in which visitors play an interactive game that presents them with a choice of two decisions in a variety of randomly generated crash scenarios. As in the trolley problem, the visitor must choose to swerve or stay the course, sacrificing either the people in the car or one group of pedestrians to save other pedestrians.
    • About Moral Machine
      • Recent scientific studies on machine ethics have raised awareness about the topic in the media and public discourse. This website aims to take the discussion further, by providing a platform for 1) building a crowd-sourced picture of human opinion on how machines should make decisions when faced with moral dilemmas, and 2) crowd-sourcing assembly and discussion of potential scenarios of moral consequence.
      • And this looks like it produced some really good marketing via news coverage
      • “We had four million users visit the website,” Awad said. “Three million of those actually completed the decision-making task, and they clicked on 37 million individual decisions. There’s also the survey that comes after, which is a little bit more work, and we still have over half a million survey responses.” The Scalable Cooperation group plans to publish the full results of the study in an upcoming paper.