Phil 1.9.18

7:00 – 4:00 ASRC MKT

  • Submit DC paper – done
  • Add primary goal and secondary goals
  • Add group decision making tool to secondary goals
  • Add site search to “standard” websearch – done
  • Visual Analytics to Support Evidence-Based Decision Making (dissertation)
  • Can Public Diplomacy Survive the internet? Bots, Echo chambers, and Disinformation
    • Shawn Powers serves as the Executive Director of the United States Advisory Commission on Public Diplomacy
    • Markos Kounalakis, Ph.D. is a visiting fellow at the Hoover Institution at Stanford University and is a presidentially appointed member of the J. William Fulbright Foreign Scholarship Board.  Kounalakis is a senior fellow at the Center for Media, Data and Society at Central European University in Budapest, Hungary and president and publisher emeritus of the Washington Monthly. He is currently researching a book on the geopolitics of global news networks.
  • Partisanship, Propaganda, and Disinformation: Online Media and the 2016 U.S. Presidential Election (Harvard)
    • Rob Faris
    • Hal Roberts
    • Bruce Etling
    • Nikki Bourassa 
    • Ethan Zuckerman
    • Yochai Benkler
    • We find that the structure and composition of media on the right and left are quite different. The leading media on the right and left are rooted in different traditions and journalistic practices. On the conservative side, more attention was paid to pro-Trump, highly partisan media outlets. On the liberal side, by contrast, the center of gravity was made up largely of long-standing media organizations steeped in the traditions and practices of objective journalism.

      Our data supports lines of research on polarization in American politics that focus on the asymmetric patterns between the left and the right, rather than studies that see polarization as a general historical phenomenon, driven by technology or other mechanisms that apply across the partisan divide.

      The analysis includes the evaluation and mapping of the media landscape from several perspectives and is based on large-scale data collection of media stories published on the web and shared on Twitter.

Advertisements

Phil 1.8.18

7:00 – 5:00 ASRC MKT

  • Complexity Explorables
    • This page is part of the Research on Complex Systems Group at the Institute for Theoretical Biology at Humboldt University of Berlin.The site is designed for people interested in complex dynamical processes. The Explorables are carefully chosen in such a way that the key elements of their behavior can be explored and explained without too much math (There are a few exceptions) and with as few words as possible.
    • Orli’s Flock’n Roll (Adjustable variables, but just having the alignment radius doesn’t have the same effect. Maybe a function of the slew rate?
      • This explorable illustrates of an intuitive dynamic model for collective motion (swarming) in animal groups. The model can be used to describe collective behavior observed in schooling fish or flocking birds, for example. The details of the model are described in a 2002 paper by Iain Couzin and colleagues.
  • Saving Human Lives: What Complexity Science and Information Systems can Contribute
    • We discuss models and data of crowd disasters, crime, terrorism, war and disease spreading to show that conventional recipes, such as deterrence strategies, are often not effective and sufficient to contain them. Many common approaches do not provide a good picture of the actual system behavior, because they neglect feedback loops, instabilities and cascade effects. The complex and often counter-intuitive behavior of social systems and their macro-level collective dynamics can be better understood by means of complexity science. We highlight that a suitable system design and management can help to stop undesirable cascade effects and to enable favorable kinds of self-organization in the system. In such a way, complexity science can help to save human lives.
  • Fooled around with the model definition section in the paper to bring forward the rate limited heading a bit.
  • Had to fix several bug in the DC paper
  • Worked with Aaron a lot on tweaking the introduction. T is reading it now. Assuming it’s done, the only thing remaining is the conclusion

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 1.3.18

Well, it didn’t take long at all for 2018 to trend radioactive…

Jan2_2018_Trump

7:00 – 4:30 ASRC MKT

  • Behavioural and Evolutionary Theory Lab. Check the publications and the venues
  • A bit on the idea that Neural Coupling is an aspect of the Willing Suspension of Disbelief.
  • More tweaking on the paper. Waaaaaayyyyyy to many “We” in the abstract. Done through modeling.
  • Need to generate nomadic, flocking, and stampede generated maps. Done! See below.
  • Redo the proposal so that the Tile View is the central navigation scheme with aspects for users, topics, ratings, etc. Done
  • Generated data for Aaron’s ML sessions. Planned upgrading my box so we can run things on the Titan card
  • Some more results from the belief space mapping effort. Each map is constructed from a 100 sample run over the same 10×10 grid after the simulation stabilized:
    • Here’s a quick overview of the populations: ThreePopulations
    • Stable Nomad behavior map: nomad-stableGood overall coverage as you would expect. Some places have more visitors (the bright spots), but there are no gaps in the belief space.
    • Stable Flocking behavior map: flocking-stableWe can see gaps start to appear in the belief space, but the overall grid structure is still visible at the center of the network where the flock spent most of its time. This is also evident in the bright ring of nodes that represents the cells that the flock traversed while it was orbiting the center area.
    • Stable stampede behavior map: stampede-stableHere, the relationship of the trajectories to the underlying coordinate frame is completely lost. In this case, the boundary of the simulation was reflective, so the stampede bounces around the simulation space. The reason that there is a loop rather than a line is because the tight cluster of agents crossed its path at some point.
  • What could be interesting it to overlay the other graphs on the nomad-produced map. We could see the popular (exploitable) sections of the flocking population while also seeing the areas visited by the stampede. The assumption is that the stampede is engaged in untrustworthy behavior, so those parts would be marked as ‘dangerous’, while the flocking areas would marked as a region of ‘conventional wisdom’ or normative behavior.

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 12.26.17

8:00 – 4:00 ASRC MKT

  • Gotta get a new keyboard
  • Working on the additional thoughts section. Add paragraph describing how the evolutionary benefits of groups are visible at nearly every level of interaction. However, with these benefits comes the additional burden of control. Evolution has provided mechanisms that are calibrated to match communication to the optimal(?) group behavior. This timeframe has been short-circuited by technology. Coordination based on the trust of a neighbor no longer works when the neighbor isn’t near.
    • Patchwork alignment?
    • Information and its use by animals in evolutionary ecology
      • Information is a crucial currency for animals from both a behavioural and evolutionary perspective. Adaptive behaviour relies upon accurate estimation of relevant ecological parameters; the better informed an individual, the better it can develop and adjust its behaviour to meet the demands of a variable world. Here, we focus on the burgeoning interest in the impact of ecological uncertainty on adaptation, and the means by which it can be reduced by gathering information, from both ‘passive’ and ‘responsive’ sources. Our overview demonstrates the value of adopting an explicitly informational approach, and highlights the components that one needs to develop useful approaches to studying information use by animals. We propose a quantitative framework, based on statistical decision theory, for analysing animal information use in evolutionary ecology. Our purpose is to promote an integrative approach to studying information use by animals, which is itself integral to adaptive animal behaviour and organismal biology.
    • Evolutionary Explanations for Cooperation
      • Natural selection favours genes that increase an organism’s ability to survive and reproduce. This would appear to lead to a world dominated by selfish behaviour. However, cooperation can be found at all levels of biological organisation: genes cooperate in genomes, organelles cooperate to form eukaryotic cells, cells cooperate to make multicellular organisms, bacterial parasites cooperate to overcome host defences, animals breed cooperatively, and humans and insects cooperate to build societies. Over the last 40 years, biologists have developed a theoretical framework that can explain cooperation at all these levels. Here, we summarise this theory, illustrate how it may be applied to real organisms and discuss future directions.
    • Thomas Valone (Scholar)
      • Much of Valone’s work in arid ecosystems has examined desertification and factors that affect the biodiversity. He is particularly interested in livestock effects on soil chemical and physical processes that then affect plant and animal populations. Valone’s examination of behavior is frequently centered on understanding how animals perceive their environment. Much of his behavioral work examines information use in social animals who differ from solitary individuals in that they can acquire public information to estimate the quality of resources by noting the activities of other individuals.
      • Group foraging, public information, and patch estimation
        • Public information is information about the quality of a patch that can be obtained by observing the foraging success of other individuals in that patch. I examine the influence of the use of public information on patch departure and foraging efficiency of group members. When groups depart a patch with the first individual to leave, the use of public information can prevent the underutilization of resource patches.
      • Public Information: From Nosy Neighbors to Cultural Evolution
        • Psychologists, economists, and advertising moguls have long known that human decision-making is strongly influenced by the behavior of others. A rapidly accumulating body of evidence suggests that the same is true in animals. Individuals can use information arising from cues inadvertently produced by the behavior of other individuals with similar requirements. Many of these cues provide public information about the quality of alternatives. The use of public information is taxonomically widespread and can enhance fitness. Public information can lead to cultural evolution, which we suggest may then affect biological evolution.
  • Get started on Polarization Game proposal. Include Moral Machine. Read the papers into LMN and started to poke at the structure.
  • Speaking of which, here’s a labeled map: LabeledMap
  • Which clearly provides more relational (map-ish) information than a word cloud using the same data: wordcloud

Phil 12.12.17

7:00 – 3:30 ASRC MKT

  • Need to make sure that an amplified agent also has amplified influence in calculating velocity – Fixed
  • Towards the end of this video is an interview with Ian Couzin talking about how mass communication is disrupting our ability to flock ‘correctly’ due to the decoupling of distance and information
  • Write up fire stampede. Backups everywhere, one hole, antennas burn so the AI keeps trust in A* but loses awareness as the antennas burn: “The Los Angeles Police Department asked drivers to avoid navigation apps, which are steering users onto more open routes — in this case, streets in the neighborhoods that are on fire.” [LA Times] Also this slow motion version of the same thing: For the Good of Society — and Traffic! — Delete Your Map App
  • First self-driving car ‘race’ ends in a crash at the Buenos Aires Formula E ePrix; two cars enter, one car survives
  • Taking a closer look at Oscillator Models and Collective Motion (178 Citations) and Consensus and Cooperation in Networked Multi-Agent Systems (6,291 Citations)
  • Consensus and Cooperation in Networked Multi-Agent Systems
    • Reza Olfati-SaberAlex Fax, and Richard M. Murray
    • We discuss the connections between consensus problems in networked dynamic systems and diverse applications including synchronization of coupled oscillators, flocking, formation control, fast consensus in small world networks, Markov processes and gossip-based algorithms, load balancing in networks, rendezvous in space, distributed sensor fusion in sensor networks, and belief propagation. We establish direct connections between spectral and structural properties of complex networks and the speed of information diffusion of consensus algorithms (Abstract)
    • In networks of agents (or dynamic systems), “consensus” means to reach an agreement regarding a certain quantity of interest that depends on the state of all agents. A “consensus algorithm” (or protocol) is an interaction rule that specifies the information exchange between an agent and all of its (nearest) neighbors on the network (pp 215)
      • In my work, this is agreement on heading and velocity
    • Graph Laplacians are an important point of focus of this paper. It is worth mentioning that the second smallest eigenvalue of graph Laplacians called algebraic connectivity quantifies the speed of convergence of consensus algorithms. (pp 216)
    • More recently, there has been a tremendous surge of interest among researchers from various disciplines of engineering and science in problems related to multi-agent networked systems with close ties to consensus problems. This includes subjects such as consensus [26]–[32], collective behavior of flocks and swarms [19], [33]–[37], sensor fusion [38]–[40], random networks [41], [42], synchronization of coupled oscillators [42]–[46], algebraic connectivity of complex networks [47]–[49], asynchronous distributed algorithms [30], [50], formation control for multi-robot systems [51]–[59], optimization-based cooperative control [60]–[63], dynamic graphs [64]–[67], complexity of coordinated tasks [68]–[71], and consensus-based belief propagation in Bayesian networks [72], [73]. (pp 216)
      • That is a dense lit review. How did they order it thematically?
    • A byproduct of this framework is to demonstrate that seemingly different consensus algorithms in the literature [10], [12]–[15] are closely related. (pp 216)
    • To understand the role of cooperation in performing coordinated tasks, we need to distinguish between unconstrained and constrained consensus problems. An unconstrained consensus problem is simply the alignment problem in which it suffices that the state of all agents asymptotically be the same. In contrast, in distributed computation of a function f(z), the state of all agents has to asymptotically become equal to f(z), meaning that the consensus problem is constrained. We refer to this constrained consensus problem as the f-consensus problem. (pp 217)
      • Normal exploring/flocking/stampeding is unconstrained. Herding adds constraint, though it’s dynamic. The variables that have to be manipulated in the case of constraint to result in the same amount of consensus are probably what’s interesting here. Examples could be how ‘loud’ does the herder have to be? Also, how ‘primed’ does the population have to be to accept herding?
    • …cooperation can be informally interpreted as “giving consent to providing one’s state and following a common protocol that serves the group objective.” (pp 217)
    • Formal analysis of the behavior of systems that involve more than one type of agent is more complicated, particularly, in presence of adversarial agents in noncooperative games [79], [80]. (pp 217)
    • The reason matrix theory [81] is so widely used in analysis of consensus algorithms [10], [12], [13], [14], [15], [64] is primarily due to the structure of P in (4) and its connection to graphs. (pp 218)
    • The role of consensus algorithms in particle based flocking is for an agent to achieve velocity matching with respect to its neighbors. In [19], it is demonstrated that flocks are networks of dynamic systems with a dynamic topology. This topology is a proximity graph that depends on the state of all agents and is determined locally for each agent, i.e., the topology of flocks is a state dependent graph. The notion of state-dependent graphs was introduced by Mesbahi [64] in a context that is independent of flocking. (pp 218)
      • They leave out heading alignment here. Deliberate? Or is heading alignment just another variant on velocity
    • Consider a network of decision-making agents with dynamics ẋi = ui interested in reaching a consensus via local communication with their neighbors on a graph G = (V, E). By reaching a consensus, we mean asymptotically converging to a one-dimensional agreement space characterized by the following equation: x1 = x2 = … = x (pp 219)
    • A dynamic graph G(t) = (V, E(t)) is a graph in which the set of edges E(t) and the adjacency matrix A(t) are time-varying. Clearly, the set of neighbors Ni(t) of every agent in a dynamic graph is a time-varying set as well. Dynamic graphs are useful for describing the network topology of mobile sensor networks and flocks [19]. (pp 219)
    • GraphLaplacianGradientDescent(pp 220)
  • algebraic connectivity of a graph: The algebraic connectivity (also known as Fiedler value or Fiedler eigenvalue) of a graph G is the second-smallest eigenvalue of the Laplacian matrix of G.[1] This eigenvalue is greater than 0 if and only if G is a connected graph. This is a corollary to the fact that the number of times 0 appears as an eigenvalue in the Laplacian is the number of connected components in the graph. The magnitude of this value reflects how well connected the overall graph is. It has been used in analysing the robustness and synchronizability of networks. (wikipedia) (pp 220)
  • According to Gershgorin theorem [81], all eigenvalues of L in the complex plane are located in a closed disk centered at delta + 0j with a radius of delta, the maximum degree of a graph (pp 220)
    • This is another measure that I can do of the nomad/flock/stampede structures combined with DBSCAN. Each agent knows what agents it is connected with, and we know how many agents there are. Each agent row should just have the number of agents it is connected to.
  • In many scenarios, networked systems can possess a dynamic topology that is time-varying due to node and link failures/creations, packet-loss [40], [98], asynchronous consensus [41], state-dependence [64], formation reconfiguration [53], evolution [96], and flocking [19], [99]. Networked systems with a dynamic topology are commonly known as switching networks. (pp 226)
  • Conclusion: A theoretical framework was provided for analysis of consensus algorithms for networked multi-agent systems with fixed or dynamic topology and directed information flow. The connections between consensus problems and several applications were discussed that include synchronization of coupled oscillators, flocking, formation control, fast consensus in small-world networks, Markov processes and gossip-based algorithms, load balancing in networks, rendezvous in space, distributed sensor fusion in sensor networks, and belief propagation. The role of “cooperation” in distributed coordination of networked autonomous systems was clarified and the effects of lack of cooperation was demonstrated by an example. It was demonstrated that notions such as graph Laplacians, nonnegative stochasticmatrices, and algebraic connectivity of graphs and digraphs play an instrumental role in analysis of consensus algorithms. We proved that algorithms introduced by Jadbabaie et al. and Fax and Murray are identical for graphs with n self-loops and are both special cases of the consensus algorithm of Olfati-Saber and Murray. The notion of Perron matrices was introduced as the discrete-time counterpart of graph Laplacians in consensus protocols. A number of fundamental spectral properties of Perron matrices were proved. This led to a unified framework for expression and analysis of consensus algorithms in both continuous-time and discrete-time. Simulation results for reaching a consensus in small-worlds versus lattice-type nearest-neighbor graphs and cooperative control of multivehicle formations were presented. (pp 231)
  • Not sure about this one. It just may be another set of algorithms to do flocking. Maybe some network implications? Flocking for Multi-Agent Dynamic Systems: Algorithms and Theory. It is one of the papers that the Consensus and Cooperation paper above leans on heavily though…
  • The Emergence of Consensus: A Primer
    • The origin of population-scale coordination has puzzled philosophers and scientists for centuries. Recently, game theory, evolutionary approaches and complex systems science have provided quantitative insights on the mechanisms of social consensus. However, the literature is vast and scattered widely across fields, making it hard for the single researcher to navigate it. This short review aims to provide a compact overview of the main dimensions over which the debate has unfolded and to discuss some representative examples. It focuses on those situations in which consensus emerges ‘spontaneously’ in absence of centralised institutions and covers topic that include the macroscopic consequences of the different microscopic rules of behavioural contagion, the role of social networks, and the mechanisms that prevent the formation of a consensus or alter it after it has emerged. Special attention is devoted to the recent wave of experiments on the emergence of consensus in social systems.
  • Critical dynamics in population vaccinating behavior
    • Complex adaptive systems exhibit characteristic dynamics near tipping points such as critical slowing down (declining resilience to perturbations). We studied Twitter and Google search data about measles from California and the United States before and after the 2014–2015 Disneyland, California measles outbreak. We find critical slowing down starting a few years before the outbreak. However, population response to the outbreak causes resilience to increase afterward. A mathematical model of measles transmission and population vaccine sentiment predicts the same patterns. Crucially, critical slowing down begins long before a system actually reaches a tipping point. Thus, it may be possible to develop analytical tools to detect populations at heightened risk of a future episode of widespread vaccine refusal.
  • For Aaron’s Social Gradient Descent Agent research (lit review)
    • On distributed search in an uncertain environment (Something like Social Gradient Descent Agents)
      • The paper investigates the case where N agents solve a complex search problem by communicating to each other their relative successes in solving the task. The problem consists in identifying a set of unknown points distributed in an n–dimensional space. The interaction rule causes the agents to organize themselves so that, asymptotically, each agent converges to a different point. The emphasis of this paper is on analyzing the collective dynamics resulting from nonlinear interactions and, in particular, to prove convergence of the search process.
    • A New Clustering Algorithm Based Upon Flocking On Complex Network (Sizing and timing for flocking systems seems to be ok?)
      • We have proposed a model based upon flocking on a complex network, and then developed two clustering algorithms on the basis of it. In the algorithms, firstly a k-nearest neighbor (knn) graph as a weighted and directed graph is produced among all data points in a dataset each of which is regarded as an agent who can move in space, and then a time-varying complex network is created by adding long-range links for each data point. Furthermore, each data point is not only acted by its k nearest neighbors but also r long-range neighbors through fields established in space by them together, so it will take a step along the direction of the vector sum of all fields. It is more important that these long-range links provides some hidden information for each data point when it moves and at the same time accelerate its speed converging to a center. As they move in space according to the proposed model, data points that belong to the same class are located at a same position gradually, whereas those that belong to different classes are away from one another. Consequently, the experimental results have demonstrated that data points in datasets are clustered reasonably and efficiently, and the rates of convergence of clustering algorithms are fast enough. Moreover, the comparison with other algorithms also provides an indication of the effectiveness of the proposed approach.
  • Done with the first draft of the white paper! And added the RFP section to the LMN productization version
  • Amazon Sage​Maker: Amazon SageMaker is a fully managed machine learning service. With Amazon SageMaker, data scientists and developers can quickly and easily build and train machine learning models, and then directly deploy them into a production-ready hosted environment. It provides an integrated Jupyter authoring notebook instance for easy access to your data sources for exploration and analysis, so you don’t have to manage servers. It also provides common machine learning algorithms that are optimized to run efficiently against extremely large data in a distributed environment. With native support for bring-your-own-algorithms and frameworks, Amazon SageMaker offers flexible distributed training options that adjust to your specific workflows. Deploy a model into a secure and scalable environment by launching it with a single click from the Amazon SageMaker console. Training and hosting are billed by minutes of usage, with no minimum fees and no upfront commitments. (from the documentation)

4:00 – 5:00 Meeting with Aaron M. to discuss Academic RB wishlist.

Phil 12.11.17

7:00 – 3:00 ASRC MKT

  • Machine learning art gallery from NIPS this year: img_20171208_212755
  • I’m reading this article on the prehistory of Bitcoin, and am realizing that there are several implications for ensuring immutability of data. For example, the entire set of records could be hashed to produce a unique has that would be disrupted if any of the records were altered.
  • Continuing Schooling as a strategy for taxis in a noisy environment here. Done! Promoted to Phlog
  • Still collecting data for web access times at work. Average time to open/finish loading a page is something around 5 seconds at work, 2 seconds at home.
  • Neural correlates of causal power judgments
    • Denise Dellarosa Cummins
    • Causal inference is a fundamental component of cognition and perception. Probabilistic theories of causal judgment (most notably causal Bayes networks) derive causal judgments using metrics that integrate contingency information. But human estimates typically diverge from these normative predictions. This is because human causal power judgments are typically strongly influenced by beliefs concerning underlying causal mechanisms, and because of the way knowledge is retrieved from human memory during the judgment process. Neuroimaging studies indicate that the brain distinguishes causal events from mere covariation, and also distinguishes between perceived and inferred causality. Areas involved in error prediction are also activated, implying automatic activation of possible exception cases during causal decision-making.
  • Writing up the Academic scenario

3:00 – 4:00 Fika – end of semester shindig

4:00 – 6:00 Meeting w/Wayne

  • Basically a status report. Maybe look at computational ecology journals if CHIIR falls through in a bad way
  • Look at workshops as well – Max Plank could be fun
  • Workshopped a workshop title with Wayne and Shimei

 

Phil 11.29.17

7:00 – 4:30 ASRC MKT

Pattern is a web mining module for the Python programming language.

  • It has tools for data mining (Google, Twitter and Wikipedia API, a web crawler, a HTML DOM parser), natural language processing (part-of-speech taggers, n-gram search, sentiment analysis, WordNet), machine learning (vector space model, clustering, SVM), network analysis and visualization.
  • Promoted Speaker–listener neural coupling underlies successful communication notes to Phlog
  • Added some bits to the JCSCW Flocking and herding article

  • Alignment in social interactions
    • According to the prevailing paradigm in social-cognitive neuroscience, the mental states of individuals become shared when they adapt to each other in the pursuit of a shared goal. We challenge this view by proposing an alternative approach to the cognitive foundations of social interactions. The central claim of this paper is that social cognition concerns the graded and dynamic process of alignment of individual minds, even in the absence of a shared goal. When individuals reciprocally exchange information about each other’s minds processes of alignment unfold over time and across space, creating a social interaction. Not all cases of joint action involve such reciprocal exchange of information. To understand the nature of social interactions, then, we propose that attention should be focused on the manner in which people align words and thoughts, bodily postures and movements, in order to take one another into account and to make full use of socially relevant information.
    • The concept of alignment has since evolved and is used to describe the multi-level, dynamic, and interactive mechanisms that underpin the sharing of people’s mental attitudes and representations in all kinds of social interactions (Dale, Fusaroli, & Duran, 2013). (pp 253)
    • The underlying justification for subsuming all these cases under the same mechanism is that cognition and action cannot be separated. The sharing of minds and bodies can then be conceptualized in terms of an integrated system of alignment, defined as the dynamic coupling of behavioural and/or cognitive states of two people (Dumas, Laroche, & Lehmann, 2014). (pp 253)
    • we are interested in the explanatory significance of alignment for a more general theory of social interaction, not in instrumental behaviour and/or alignment per se. (pp 254)
    • The central claim of this paper is that the alignment of minds, which emerges in social interactions, involves the reciprocal exchange of information whereby individuals adjust minds and bodies in a graded and dynamic manner. As these processes of alignment unfold, interacting partners will exchange information about each other’s minds and therefore act socially, whether or not a shared goal is in place. (pp 254)
    • In particular, in recent theoretical and empirical work on social cognition, reciprocity is increasingly recognized as a useful resource to capture the ‘‘jointness” of a joint action. Interpersonal understanding can be achieved by reading into one another’s mind reciprocally (Butterfill, 2013), and an explanation of the processes whereby the alignment of minds and bodies unfolds in space and time should involve an account of reciprocity (Zahavi & Rochat, 2015). In the process of a reciprocal exchange of information, individuals may adapt to varying degrees to one another. This is certainly the case in instances of temporal synchronisation and coordination in which physical alignment in time and space has been theorized to depend on cognitive models of adaptation (Elliott, Chua, & Wing, 2016; Hayashi & Kondo, 2013; Repp & Su, 2013) and thus on reciprocal interactions (D’Ausilio, Novembre, Fadiga, & Keller, 2015; Keller, Novembre, & Hove, 2014; Tognoli & Kelso, 2015). The behaviour of one player results in a change in behaviour of the other in a reciprocal way so as to achieve temporal synchrony. Interestingly, though not surprisingly, this reciprocal exchange of information results in physical alignment, which in turn has also been shown to result in greater degrees of affiliation and greater mental alignment (Hove & Risen, 2009; Rabinowitch & Knafo-Noam, 2015; Wiltermuth & Heath, 2009). Specifically, we suggest that, rather than a focus on the sharedness of the intended goal, we should attend to the graded exchange of information that creates alignment. The most social of interactions, in our formulation, are those in which ‘‘live” (‘‘online”, see Schilbach, 2014) information is exchanged dynamically (i.e. over time, across multiple points) bidirectionally and used to adapt behaviour and align with another (Jasmin et al., 2016). (pp 255)
    • Indeed, it is possible to have reciprocity and thus social interaction without cooperation. This would be the case, for example, in a competitive scenario in which the minds of the subjects are aligned at the appropriate level of description, and the sharing is essential to solve social dilemmas involving antagonistic behaviour (Bratman, 2014). In these exchanges, what is needed for the minds of the agents to attune to one another is that they adapt thoughts, bodily postures and movements, to take one another into account and reason as a team, even though the team might consist of competitive actors where none is aware that they are acting from the perspective of the same group and in the pursuit of some common goal (Bacharach, 2006). (pp 255)
    • fundamentally social nature has to do with the process whereby systems reciprocate thoughts and experiences, rather than with the endpoint i.e. the goal. It turns out that two features are often taken to be central to the process whereby interacting agents align minds and bodies. First, the interacting agents must be aware that they are doing something together with others. Second, the success of their joint performance is taken as a measure of how shared the participants’ goals are. (pp 255)
    • our suggestion is that what matters for the relevant alignment of minds and bodies to occur is the reciprocal exchange of information, not awareness of the reciprocal exchange of information. (pp 255)
      • This is all that is needed for flocking to happen. It is the range of that exchange that determines the phase change from independent to flock to stampede. Trust is involved in the reciprocity too, I think
    • Becoming mutually aware that we are sharing attitudes, dispositions, bodily postures, perhaps goals, does not mean that the ‘jointness’ of our actions has become available to each of us for conscious report. Reciprocity of awareness is emphatically not the same as awareness of reciprocity. The process of reciprocally exchanging information and mutually adapting to one another need not necessarily result in any degree of shared awareness. (pp 256)
    • In animals, a signal, for example about the source of food, that is too weak for an individual fish to follow can be followed by a group through the simple rules of bodily alignment that create shoaling behaviour (Grunbaum, 1998). Shoaling behaviour can also be observed in humans (Belz, Pyritz, & Boos, 2013), who can achieve group advantage through more complex forms of adjustment than just bodily alignment. Pairs of participants trying to detect a weak visual signal can achieve a greater group advantage when they align the terms they use to report their confidence in what they saw (Fusaroli et al., 2012). Indeed, linguistic alignment at many levels can be observed in dialogue (Pickering & Garrod, 2004) and can improve comprehension (Adank, Hagoort, & Bekkering, 2010; Fusaroli et al., 2012). (pp 256)
    • Much research has been driven, so far, by the implicit goal of identifying optimal group performance as a proxy for mental alignment (Fusaroli et al., 2012), however, there is conceptual room and empirical evidence for arguing that optimal task performance is not a good index of mental alignment or ‘optimal sociality’. In other words, taking achievement of a shared goal as the paradigm of a social interaction leads to the binary conception of sociality according to which an interaction is either (optimally) social, or it is not. (pp 256)
      • This is a problem that I have with opinion dynamics models. Convergence on a particular opinion isn’t the only issue. There is a dynamic process where opinions fall in and out of favor. This is the difference between the contagion model, which is one way (uninfected->infected) and motion through belief space. The goal really doesn’t matter, except in a subset of cases (Though these may be very important)
    • Two systems can interact when they have access to information relating to each other (Bilek et al., 2015). There are different ways of exchanging information between systems and hence different types of interaction (Liu & Pelowski, 2014), but in every case some kind of alignment occurs (Coey, Varlet, & Richardson, 2012; Huygens, 1673). (pp 257)
    • Such offline interaction can be contrasted with the case of online social interactions, where both participants act. The distinction between offline and online social interaction tasks is now acknowledged as crucial for advancing our understanding of the cognition processes underlying social interaction (Schilbach, 2014). (pp 257)
    • In contrast to salsa, consider the case of tango in which movements are improvised and as such require constant, mutual adaptation (Koehne et al., 2015; Tateo, 2014). Tango dancers have access to information relating to each other and, by virtue of the task, they exchange information with one another across time in a reciprocal and bidirectional fashion. The juxtaposition of tango with salsa highlights a spectrum of degrees of mutual reciprocity, with a richer form of interaction and greater need for alignment in tango compared with salsa.
    • we will take reciprocity to be the primary requirement for social interactions. We suggest that reciprocity can be identified with a special kind of alignment, mutual alignment, involving adjustment in both parties to the interaction. However, not all cases of joint action lead to mutual alignment. It is important to distinguish this mutual alignment from other types of alignment, which do not involve a reciprocal exchange of information between the agents. (pp 257)
    • In contrast to salsa, consider the case of tango in which movements are improvised and as such require constant, mutual adaptation (Koehne et al., 2015; Tateo, 2014). Tango dancers have access to information relating to each other and, by virtue of the task, they exchange information with one another across time in a reciprocal and bidirectional fashion. The juxtaposition of tango with salsa highlights a spectrum of degrees of mutual reciprocity, with a richer form of interaction and greater need for alignment in tango compared with salsa. (pp 257)
    • AlignmentInSocialInteractions(pp 258)
    • The biggest challenge currently facing philosophers and scientists of social cognition is to understand social interactions. We suggest that this problem is best approached at the level of processes of mental alignment rather than through joint action tasks based on shared goals, and we propose that the key process is one of reciprocal, dynamic and graded adaptation between the participants in the interaction. Defining social interactions in terms of reciprocal patterns of alignment shows that not all joint actions involve reciprocity and also that social interactions can occur in the absence of shared goals. This approach has two particular advantages. First, it emphasises the key point that interactions can only be fully understood at the level of the group, rather than the individual. The pooling together of individual mental resources generates results that exceed the sum of the individual contributions. But, second, our approach points towards the mechanisms of adaptation that must be occurring within each individual in order to create the interaction (Friston & Frith, 2015). (pp 259)
    • This picture of social interaction in terms of mental alignment suggests two important theoretical developments. One is about a possible way to characterize the idea that types of social interaction lie on a continuum of possible solutions. If we focus on the task or the shared goal being pursued by agents jointly, as the current literature suggests, then only limited subdivisions of types of interaction will emerge. If, however, our focus extends so as to integrate the nature of the interaction, conceived of in terms of information exchange, then we can arrive at a higher degree of resolution of the space in which social interaction lie. This will define a spectrum of types of interaction (not just offline versus online social cognition), suggesting a dimensional rather than a discrete picture. After all, alignment comes in degrees and a spectrum-like definition of sociality implies that there is a variety of forms of alignment and hence of interactions. (pp 269)
      • My work would indicate that meaningful transitions occur for Unaligned (pure explore), Complex (flocking), and Total (stampede).
  • Continuing to work on The Socio-Temporal Brain: Connecting People in Time here
    • Not as good as I thought it would be. Some useful items, but there is no brain analysis of chorusing animals, just the co-mention
  • Continuing Research Browser white paper. Added note to work through linking multiple tags to the same item with visibility controls. Kindle has a feature like this.
  • Reading section 16.7 on personalized web services  (pp 372 – 375) for words and concepts for Augmented Data Discovery. Then Where to Add Actions in Human-in-the-Loop Reinforcement LearningPolicy Shaping: Integrating Human Feedback with Reinforcement Learning, and AXIS: Generating Explanations at Scale with Learner sourcing and Machine Learning

 

Phil 11.28.17

7:00 – 8:00 Research, 8:30 – 4:30 ASRC MKT

  • Continuing Speaker–listener neural coupling underlies successful communication here. Done! Will promote to Phlog later.
  • Collective cognition in animal groups
    • Iain Couzin
    • The remarkable collective action of organisms such as swarming ants, schooling fish and flocking birds has long captivated the attention of artists, naturalists, philosophers and scientists. Despite a long history of scientific investigation, only now are we beginning to decipher the relationship between individuals and group-level properties. This interdisciplinary effort is beginning to reveal the underlying principles of collective decision-making in animal groups, demonstrating how social interactions, individual state, environmental modification and processes of informational amplification and decay can all play a part in tuning adaptive response. It is proposed that important commonalities exist with the understanding of neuronal processes and that much could be learned by considering collective animal behavior in the framework of cognitive science.
  • The Socio-Temporal Brain: Connecting People in Timethis looks like it might explicitly link human neural coupling and flocking.
    • Temporal and social processing are intricately linked. The temporal extent and organization of interactional behaviors both within and between individuals critically determine interaction success. Conversely, social signals and social context influence time perception by, for example, altering subjective duration and making an event seem ‘out of sync’. An ‘internal clock’ involving subcortically orchestrated cortical oscillations that represent temporal information, such as duration and rhythm, as well as insular projections linking temporal information with internal and external experiences is proposed as the core of these reciprocal interactions. The timing of social relative to non-social stimuli augments right insular activity and recruits right superior temporal cortex. Together, these reciprocal pathways may enable the exchange and respective modulation of temporal and social computations.
    • timing is not encapsulated but interacts closely with the social processes that emerge from interpersonal interactions [1–3]. As interactional behaviors play out in time, their temporal signatures carry important information. They guide attention, convey a message, and mold bonds between individuals. Moreover, interactional behaviors in turn give meaning to time and influence its perception and representation. A range of disorders that jointly compromise temporal and social processes attest to this relation (pp 760)
    • We review here the many ways in which timing intersects with social processing. We explore this intersection for the communicative behavior of an individual as well as for the behavioral coordination between communicating agents. We detail neuroimaging evidence on how temporal and social information are represented in the brain and identify points of structural and functional convergence (pp 760)
    • The temporal coordination of behavior in animals is referred to as chorusing. Chorusing describes sporadic behaviors such those of flying birds (Figure I), which may change direction and speed in a manner resembling a single superorganism [87]. In addition, it describes behaviors that occur repeatedly and with some amount of temporal regularity, as in the mating calls of male frogs. Although less frequent than sporadic chorusing, rhythmic chorusing is displayed by a range of taxa including insects, reptiles, birds, and mammals [88, 89]. (pp 761)
    • Research into the functionality of chorusing suggests species differences. For some, it seems to be a mere byproduct of competitive interactions. For others, it reduces the risk of predation [98]. By analogy with the selfish-herd principle, overlapping with others in time makes it harder for predators to single out individual prey. Last, there are species in which chorusing serves as a fitness display in the context of sexual selection [99] and as a means to foster social bonds [100]. Because of its pervasiveness and social context, some suggest chorusing to be the driving evolutionary force for a species’ timing sense [88]. (pp 761)
    • The degree of temporal coordination between interaction partners relates to interaction success. For example, it produces affective consequences [21]. This was revealed by a study in which pairs of strangers discussed four topics and completed an affective state questionnaire before and after each topic. Results provided evidence for emerging synchrony between discussion partners (Figure 1) and for its causal effect on ensuing positive affect. Related research showed that individuals more readily empathize with a synchronous as compared to a non-synchronous partner [22] and that synchronous dyads are more creative [23] and trusting [24] than nonsynchronous dyads. (pp 762)
  • Word Embeddings Quantify 100 Years of Gender and Ethnic Stereotypes
    • Word embeddings use vectors to represent words such that the geometry between vectors captures semantic relationship between the words. In this paper, we develop a framework to demonstrate how the temporal dynamics of the embedding can be leveraged to quantify changes in stereotypes and attitudes toward women and ethnic minorities in the 20th and 21st centuries in the United States. We integrate word embeddings trained on 100 years of text data with the U.S. Census to show that changes in the embedding track closely with demographic and occupation shifts over time. The embedding captures global social shifts – e.g., the women’s movement in the 1960s and Asian immigration into the U.S – and also illuminates how specific adjectives and occupations became more closely associated with certain populations over time. Our framework for temporal analysis of word embedding opens up a powerful new intersection between machine learning and quantitative social science.
    • bias
    • I think this is really close to the belief trajectories I’m trying to tease out. In the figures above, note that it is possible to extract both trajectories and normative terms. Plus, the paper has a really good writeup of methods in the appendices.
  • Continuing to write up use cases/RB proposal
  • Also still doing stuff for the HHS RFI
  • Relevant to the Research Browser:
    • Extracting Automata from Recurrent Neural Networks Using Queries and Counterexamples <- Agent generation is a thing!
      • We address the problem of extracting an automaton from a trained recurrent neural network (RNN). We present a novel algorithm that uses exact learning and abstract interpretation to perform efficient extraction of a minimal automaton describing the state dynamics of a given RNN. We use Angluin’s L* algorithm as a learner and the given RNN as an oracle, employing abstract interpretation of the RNN for answering equivalence queries. Our technique allows automaton-extraction from the RNN while avoiding state explosion, even when the state vectors are large and fine differentiation is required between RNN states. 
        We experiment with automata extraction from multi-layer GRU and LSTM based RNNs, with state-vector dimensions and underlying automata and alphabet sizes which are significantly larger than in previous automata-extraction work. In some cases, the underlying target language can be described with a succinct automata, yet the extracted automata is large and complex. These are cases in which the RNN failed to learn the intended generalization, and our extraction procedure highlights words which are misclassified by the seemingly “perfect” RNN.
    • Policy Shaping: Integrating Human Feedback with Reinforcement Learning
      • A long term goal of Interactive Reinforcement Learning is to incorporate nonexpert human feedback to solve complex tasks. Some state-of-the-art methods have approached this problem by mapping human information to rewards and values and iterating over them to compute better control policies. In this paper we argue for an alternate, more effective characterization of human feedback: Policy Shaping. We introduce Advise, a Bayesian approach that attempts to maximize the information gained from human feedback by utilizing it as direct policy labels. We compare Advise to state-of-the-art approaches and show that it can outperform them and is robust to infrequent and inconsistent human feedback.
    • AXIS: Generating Explanations at Scale with Learnersourcing and Machine Learning
      • While explanations may help people learn by providing information about why an answer is correct, many problems on online platforms lack high-quality explanations. This paper presents AXIS (Adaptive eXplanation Improvement System), a system for obtaining explanations. AXIS asks learners to generate, revise, and evaluate explanations as they solve a problem, and then uses machine learning to dynamically determine which explanation to present to a future learner, based on previous learners’ collective input. Results from a case study deployment and a randomized experiment demonstrate that AXIS elicits and identifies explanations that learners find helpful. Providing explanations from AXIS also objectively enhanced learning, when compared to the default practice where learners solved problems and received answers without explanations. The rated quality and learning benefit of AXIS explanations did not differ from explanations generated by an experienced instructor.