# Phil 3.23.18

7:00 – 5:00 ASRC MKT

• Influence of augmented humans in online interactions during voting events
• Overwhelming empirical evidence has shown that online social dynamics mirrors real-world events. Hence, understanding the mechanisms leading to social contagion in online ecosystems is fundamental for predicting, and even manouvering, human behavior. It has been shown that one of such mechanisms is based on fabricating armies of automated agents that are known as social bots. Using the recent Italian elections as an emblematic case study, here we provide evidence for the existence of a special class of highly influential users, that we name “augmented humans”. They exploit bots for enhancing both their visibility and influence, generating deep information cascades to the same extent of news media and other broadcasters. Augmented humans uniformly infiltrate across the full range of identified clusters of accounts, the latter reflecting political parties and their electoral ranks.
• Reddit and the Struggle to Detoxify the Internet
• “Does free speech mean literally anyone can say anything at any time?” Tidwell continued. “Or is it actually more conducive to the free exchange of ideas if we create a platform where women and people of color can say what they want without thousands of people screaming, ‘Fuck you, light yourself on fire, I know where you live’? If your entire answer to that very difficult question is ‘Free speech,’ then, I’m sorry, that tells me that you’re not really paying attention.”
• This is the difference between discussion and stampede. That seems like it should be statistically detectable.
• Metabolic Costs of Feeding Predictively Alter the Spatial Distribution of Individuals in Fish Schools
• We examined individual positioning in groups of swimming fish after feeding
• Fish that ate most subsequently shifted to more posterior positions within groups
• Shifts in position were related to the remaining aerobic scope after feeding
• Feeding-related constraints could affect leadership and group functioning
• I wonder if this also keeps the hungrier fish at the front, increasing the effectiveness of gradient detections
• Listening to Invisibilia: The Pattern Problem. There is a section on using machine learning for sociology. Listening to get the author of the ML and Sociology study. Predictions were not accurate. Not published?
• The Coming Information Totalitarianism in China
• The real-name system has two purposes. One is the chilling effect, and it works very well on average netizens but not so much on activists. The other and the main purpose is to be able to locate activists and eliminate them from certain information/opinion platforms, in the same way that opinions of dissident intellectuals are completely eradicated from the traditional media.
• More BIC – Done! Need to assemble notes
• It is a central component of resolute choice, as presented by McClennen, that (unless new information becomes available) later transient agents recognise the authority of plans made by earlier agents. Being resolute just is recognising that authority (although McClennen’ s arguments for the rationality and psychological feasibility of resoluteness apply only in cases in which the earlier agents’ plans further the common ends of earlier and later agents). This feature of resolute choice is similar to Bacharach’ s analysis of direction, explained in section 5. If the relationship between transient agents is modelled as a sequential game, resolute choice can be thought of as a form of direction, in which the first transient agent plays the role of director; the plan chosen by that agent can be thought of as a message sent by the director to the other agents. To the extent that each later agent is confident that this plan is in the best interests of the continuing person, that confidence derives from the belief that the first agent identified with the person and that she was sufficiently rational and informed to judge which sequence of actions would best serve the person’s objectives. (pg 197)
• Meeting with celer scientific
• More TF with Keras. Really good progress

# Phil 3.22.18

7:00 – 5:00 ASRC MKT

• The ONR proposal is in!
• Promoted the Odyssey thoughts to Phlog
• More BIC
• The problem posed by Heads and Tails is not that the players lack a common understanding of salience; it is that game theory lacks an adequate explanation of how salience affects the decisions of rational players. All we gain by adding preplay communication to the model is the realisation that game theory also lacks an adequate explanation of how costless messages affect the decisions of rational players. (pg 180)
• More TF crash course
• Invert the ratio for train and validation
• Add the check against test data
• Get started on LSTM w/Aaron?

# Phil 3.20.18

7:00 – 3:00 ASRC MKT

• What (satirical) denying a map looks like. Nice application of believability.
• Need to make a folder with all the CUDA bits and Visual Studio to get all my boxes working with GPU tensorflow
• Assemble one-page resume for ONR proposal
• More BIC
• The fundamental principle of this morality is that what each agent ought to do is to co-operate, with whoever else is co-operating, in the production of the best consequences possible given the behaviour of non-co-operators’ (Regan 1980, p. 124). (pg 167)
• Ordered On Social Facts
• Are social groups real in any sense that is independent of the thoughts, actions, and beliefs of the individuals making up the group? Using methods of philosophy to examine such longstanding sociological questions, Margaret Gilbert gives a general characterization of the core phenomena at issue in the domain of human social life.

Back to the TF crash course

• Had to update my numpy from Christoph Gohlke’s Unofficial Windows Binaries for Python Extension Packages. It’s wonderful, but WHY???
• Also had this problem updating numpy
D:\installed>pip3 install "numpy-1.14.2+mkl-cp37-cp37m-win_amd64.whl"
numpy-1.14.2+mkl-cp37-cp37m-win_amd64.whl is not a supported wheel on this platform.
• That was solved by installing numpy-1.14.2+mkl-cp36-cp36m-win_amd64.whl. Why cp36 works and cp 37 doesn’t is beyond me.
• Left early due to snow

# Phil 3.19.18

7:00 – 5:00 ASRC MKT

• The Perfect Selfishness of Mapping Apps
• Apps like Waze, Google Maps, and Apple Maps may make traffic conditions worse in some areas, new research suggests.
• Cambridge Social Decision-Making Lab
• More BIC
• Schema 3: Team reasoning (from a group viewpoint) pg 153
• We are the members of S.
• Each of us identifies with S.
• Each of us wants the value of U to be maximized.
• A uniquely maximizes U.
• Each of us should choose her component of A.
• Schema 4: Team reasoning (from an individual viewpoint) pg 159
• I am a member of S.
• It is common knowledge in S that each member of S identifies
with S.
• It is common knowledge in S that each member of S wants the
value of U to be maximized.
• It is common knowledge in S that A uniquely maximizes U.
• I should choose my component of A.
• Schema 7: Basic team reasoning pg 161
• I am a member of S.
• It is common knowledge in S that each member of S identifies
with S.
• It is common knowledge in S that each member of S wants the
value of U to be maximized.
• It is common knowledge in S that each member of S knows his
component of the profile that uniquely maximizes U.
• I should choose my component of the profile that uniquely
maximizes U.

• Bacharach notes to himself the ‘hunch’ that this schema is ‘the basic rational capacity’ which leads to high in Hi-Lo, and that it ‘seems to be indispensable if a group is ever to choose the best plan in the most ordinary organizational circumstances’. Notice that Schema 7 does not require that the individual who uses it know everyone’s component of the profile that maximizes U.
• His hypothesis is that group identification is an individual’s psychological response to the stimulus of a particular decision situation. It is not in itself a group action. (To treat it as a group action would, in Bacharach’ s framework, lead to an infinite regress.) In the theory of circumspect team reasoning, the parameter w is interpreted as a property of a psychological mechanism-the probability that a person who confronts the relevant stimulus will respond by framing the situation as a problem ‘for us’. The idea is that, in coming to frame the situation as a problem ‘for us’, an individual also gains some sense of how likely it is that another individual would frame it in the same way; in this way, the value of w becomes common knowledge among those who use this frame. (Compare the case of the large cube in the game of Large and Small Cubes, discussed in section 4 of the introduction.) Given this model, it seems that the ‘us’ in terms of which the problem is framed must be determined by how the decision situation first appears to each individual. Thus, except in the special case in which w == 1, we must distinguish S (the group with which individuals are liable to identify, given the nature of the decision situation) from T (the set of individuals who in fact identify with S). pg 163
C:\WINDOWS\system32>pip3 install --upgrade tensorflow-gpu
Collecting tensorflow-gpu
100% |████████████████████████████████| 85.9MB 17kB/s
Collecting termcolor>=1.1.0 (from tensorflow-gpu)
Collecting absl-py>=0.1.6 (from tensorflow-gpu)
100% |████████████████████████████████| 81kB 6.1MB/s
Collecting grpcio>=1.8.6 (from tensorflow-gpu)
100% |████████████████████████████████| 1.3MB 1.1MB/s
Collecting numpy>=1.13.3 (from tensorflow-gpu)
100% |████████████████████████████████| 13.4MB 121kB/s
Collecting astor>=0.6.0 (from tensorflow-gpu)
Requirement already up-to-date: six>=1.10.0 in c:\program files\python36\lib\site-packages (from tensorflow-gpu)
Collecting tensorboard<1.7.0,>=1.6.0 (from tensorflow-gpu)
100% |████████████████████████████████| 3.1MB 503kB/s
Collecting protobuf>=3.4.0 (from tensorflow-gpu)
100% |████████████████████████████████| 962kB 1.3MB/s
Collecting gast>=0.2.0 (from tensorflow-gpu)
Requirement already up-to-date: wheel>=0.26 in c:\program files\python36\lib\site-packages (from tensorflow-gpu)
Requirement already up-to-date: html5lib==0.9999999 in c:\program files\python36\lib\site-packages (from tensorboard<1.7.0,>=1.6.0->tensorflow-gpu)
Requirement already up-to-date: bleach==1.5.0 in c:\program files\python36\lib\site-packages (from tensorboard<1.7.0,>=1.6.0->tensorflow-gpu)
Requirement already up-to-date: markdown>=2.6.8 in c:\program files\python36\lib\site-packages (from tensorboard<1.7.0,>=1.6.0->tensorflow-gpu)
Requirement already up-to-date: werkzeug>=0.11.10 in c:\program files\python36\lib\site-packages (from tensorboard<1.7.0,>=1.6.0->tensorflow-gpu)
Collecting setuptools (from protobuf>=3.4.0->tensorflow-gpu)
100% |████████████████████████████████| 573kB 2.3MB/s
Building wheels for collected packages: termcolor, absl-py, gast
Running setup.py bdist_wheel for termcolor ... done
Stored in directory: C:\Users\philip.feldman\AppData\Local\pip\Cache\wheels\de\f7\bf\1bcac7bf30549e6a4957382e2ecab04c88e513117207067b03
Running setup.py bdist_wheel for absl-py ... done
Stored in directory: C:\Users\philip.feldman\AppData\Local\pip\Cache\wheels\3c\0f\0a\6c94612a8c26070755559045612ca3645fea91c11f2148363e
Running setup.py bdist_wheel for gast ... done
Stored in directory: C:\Users\philip.feldman\AppData\Local\pip\Cache\wheels\8e\fa\d6\77dd17d18ea23fd7b860e02623d27c1be451521af40dd4a13e
Successfully built termcolor absl-py gast
Installing collected packages: termcolor, absl-py, setuptools, protobuf, grpcio, numpy, astor, tensorboard, gast, tensorflow-gpu
Found existing installation: setuptools 38.4.0
Uninstalling setuptools-38.4.0:
Successfully uninstalled setuptools-38.4.0
Found existing installation: protobuf 3.5.1
Uninstalling protobuf-3.5.1:
Successfully uninstalled protobuf-3.5.1
Found existing installation: numpy 1.13.0+mkl
Uninstalling numpy-1.13.0+mkl:
Successfully uninstalled numpy-1.13.0+mkl
Found existing installation: tensorflow-gpu 1.4.0
Uninstalling tensorflow-gpu-1.4.0:
Successfully uninstalled tensorflow-gpu-1.4.0
Successfully installed absl-py-0.1.11 astor-0.6.2 gast-0.2.0 grpcio-1.10.0 numpy-1.14.2 protobuf-3.5.2.post1 setuptools-39.0.1 tensorboard-1.6.0 tensorflow-gpu-1.6.0 termcolor-1.1.0
• That caused the following items to break when I tried running “fully_connected.py”
"C:\Program Files\Python36\python.exe" D:/Development/Sandboxes/TensorflowPlayground/HelloPackage/fully_connected_feed.py
Traceback (most recent call last):
File "C:\Program Files\Python36\lib\site-packages\tensorflow\python\platform\self_check.py", line 75, in preload_check
ctypes.WinDLL(build_info.cudart_dll_name)
File "C:\Program Files\Python36\lib\ctypes\__init__.py", line 348, in __init__
self._handle = _dlopen(self._name, mode)
OSError: [WinError 126] The specified module could not be found

During handling of the above exception, another exception occurred:

Traceback (most recent call last):
File "D:/Development/Sandboxes/TensorflowPlayground/HelloPackage/fully_connected_feed.py", line 28, in
import tensorflow as tf
File "C:\Program Files\Python36\lib\site-packages\tensorflow\__init__.py", line 24, in
from tensorflow.python import *
File "C:\Program Files\Python36\lib\site-packages\tensorflow\python\__init__.py", line 49, in
from tensorflow.python import pywrap_tensorflow
File "C:\Program Files\Python36\lib\site-packages\tensorflow\python\pywrap_tensorflow.py", line 30, in
File "C:\Program Files\Python36\lib\site-packages\tensorflow\python\platform\self_check.py", line 82, in preload_check
% (build_info.cudart_dll_name, build_info.cuda_version_number))
ImportError: Could not find 'cudart64_90.dll'. TensorFlow requires that this DLL be installed in a directory that is named in your %PATH% environment variable. Download and install CUDA 9.0 from this URL: https://developer.nvidia.com/cuda-toolkit
• Installing Visual Studio for the DLLs before I install the Cuda parts
• Next set of errors
Traceback (most recent call last):
File "D:/Development/Sandboxes/TensorflowPlayground/HelloPackage/fully_connected_feed.py", line 28, in
import tensorflow as tf
File "C:\Program Files\Python36\lib\site-packages\tensorflow\__init__.py", line 24, in
from tensorflow.python import *
File "C:\Program Files\Python36\lib\site-packages\tensorflow\python\__init__.py", line 49, in
from tensorflow.python import pywrap_tensorflow
File "C:\Program Files\Python36\lib\site-packages\tensorflow\python\pywrap_tensorflow.py", line 30, in
File "C:\Program Files\Python36\lib\site-packages\tensorflow\python\platform\self_check.py", line 97, in preload_check
% (build_info.cudnn_dll_name, build_info.cudnn_version_number))
ImportError: Could not find 'cudnn64_7.dll'. TensorFlow requires that this DLL be installed in a directory that is named in your %PATH% environment variable. Note that installing cuDNN is a separate step from installing CUDA, and this DLL is often found in a different directory from the CUDA DLLs. You may install the necessary DLL by downloading cuDNN 7 from this URL: https://developer.nvidia.com/cudnn

• Looking for cudnn64_7.dll here?
• Aaaand that seems to be working!
• Tweaked ONR proposal with Aaron. Discovered that there is one page per PI, so we need to make one-page resumes.

# Phil 3.16.18

7:00 – 4:00 ASRC MKT

• Umwelt
• In the semiotic theories of Jakob von Uexküll and Thomas A. Sebeokumwelt (plural: umwelten; from the German Umwelt meaning “environment” or “surroundings”) is the “biological foundations that lie at the very epicenter of the study of both communication and signification in the human [and non-human] animal”.[1] The term is usually translated as “self-centered world”.[2] Uexküll theorised that organisms can have different umwelten, even though they share the same environment. The subject of umwelt and Uexküll’s work is described by Dorion Sagan in an introduction to a collection of translations.[3] The term umwelt, together with companion terms umgebungand innenwelt, have special relevance for cognitive philosophers, roboticists and cyberneticians, since they offer a solution to the conundrum of the infinite regress of the Cartesian Theater.
• Benjamin Kuipers
• How Can We Trust a Robot? (video)
• Advances in artificial intelligence (AI) and robotics have raised concerns about the impact on our society of intelligent robots, unconstrained by morality or ethics
• Socially-Aware Navigation Using Topological Maps and Social Norm Learning
• We present socially-aware navigation for an intelligent robot wheelchair in an environment with many pedestrians. The robot learns social norms by observing the behaviors of human pedestrians, interpreting detected biases as social norms, and incorporating those norms into its motion planning. We compare our socially-aware motion planner with a baseline motion planner that produces safe, collision-free motion. The ability of our robot to learn generalizable social norms depends on our use of a topological map abstraction, so that a practical number of observations can allow learning of a social norm applicable in a wide variety of circumstances. We show that the robot can detect biases in observed human behavior that support learning the social norm of driving on the right. Furthermore, we show that when the robot follows these social norms, its behavior influences the behavior of pedestrians around it, increasing their adherence to the same norms. We conjecture that the legibility of the robot’s normative behavior improves human pedestrians’ ability to predict the robot’s future behavior, making them more likely to follow the same norm.
• Erin’s defense
• Nice slides!
• Slide 4 – narrowing from big question to dissertation topic. Nice way to set up framing
• Intellectual function vs. adaptive behavior
• Loss of self-determination
• Maker culture as a way of having your own high-dimensional vector? Does this mean that the maker culture is inherently more exploratory when compared to …?
• “Frustration is an easy way to end up in off-task behavior”
• Peer learning as gradient descent?
• Emic ethnography
• Pervasive technology in education
• Turn-taking
• Antecedent behavior consequence theory
• Reducing the burden on the educators. Low-level detection and to draw attention to the educator and annotate. Capturing and labeling
• Helina – bring the conclusions back to the core questions
• Diversity injection works! Mainstream students gained broader appreciation of students with disability
• Q: Does it make more sense to focus on potentially charismatic technologies that will include the more difficult outliers even if it requires a breakthrough? Or to make incremental improvements that can improve accessibility to some people with disabilities faster?
• Boris analytic software

# Phil 3.8.18

7:00 – 5:00 ASRC

• Another nice comment from Joanna Bryson on BBC Business Daily – The bias is seldom in the algorithm. Latent Semantic Indexing is simple arithmetic. The data contains the bias, and that’s from us. Fairness is a negotiated concept, which means that is is complicated. Requiring algorithmic fairness necessitates placing enormous power in the hands of those writing the algorithms.
• The science of fake news (Science magazine)
• The rise of fake news highlights the erosion of long-standing institutional bulwarks against misinformation in the internet age. Concern over the problem is global. However, much remains unknown regarding the vulnerabilities of individuals, institutions, and society to manipulations by malicious actors. A new system of safeguards is needed. Below, we discuss extant social and computer science research regarding belief in fake news and the mechanisms by which it spreads. Fake news has a long history, but we focus on unanswered scientific questions raised by the proliferation of its most recent, politically oriented incarnation. Beyond selected references in the text, suggested further reading can be found in the supplementary materials.
• Incorporating Sy’s comments into a new slide deck
• More ONR
• Meeting with Shimei
• Definitely use the ONR-specified headings
• Research is looking good and interesting! Had to spend quite a while explaining lexical trajectories.
• Ran through the slides with Sy again. Mostly finalized?

# Phil 3.7.18

7:00 – 5:00 ASRC MKT

• Some surprising snow
• Meeting with Sy at 1:30 slides
• Meeting with Dr. DesJardins at 4:00
• Nice chat with Wajanat about the presentation of the Saudi Female self in physical and virtual environments
• Sprint planning
• Finish ONR Proposal VP-331
• CHIIR VP-332
• Prep for TF dev conf VP-334
• TF dev conf VP-334
• Working on the ONR proposal
• Oxford Internet Institute – Computational Propaganda Research Project
• The Computational Propaganda Research Project (COMPROP) investigates the interaction of algorithms, automation and politics. This work includes analysis of how tools like social media bots are used to manipulate public opinion by amplifying or repressing political content, disinformation, hate speech, and junk news. We use perspectives from organizational sociology, human computer interaction, communication, information science, and political science to interpret and analyze the evidence we are gathering. Our project is based at the Oxford Internet Institute, University of Oxford.
• Polarization, Partisanship and Junk News Consumption over Social Media in the US
• What kinds of social media users read junk news? We examine the distribution of the most significant sources of junk news in the three months before President Donald Trump’s first State of the Union Address. Drawing on a list of sources that consistently publish political news and information that is extremist, sensationalist, conspiratorial, masked commentary, fake news and other forms of junk news, we find that the distribution of such content is unevenly spread across the ideological spectrum. We demonstrate that (1) on Twitter, a network of Trump supporters shares the widest range of known junk news sources and circulates more junk news than all the other groups put together; (2) on Facebook, extreme hard right pages—distinct from Republican pages—share the widest range of known junk news sources and circulate more junk news than all the other audiences put together; (3) on average, the audiences for junk news on Twitter share a wider range of known junk news sources than audiences on Facebook’s public pages
• Need to look at the variance in the articles. Are these topical stampedes? Or is this source-oriented?
• Understanding and Addressing the Disinformation Ecosystem
• This workshop brings together academics, journalists, fact-checkers, technologists, and funders to better understand the challenges produced by the current disinformation ecosystem. The facilitated discussions will highlight relevant research, share best-practices, identify key questions of scholarly and practical concern regarding the nature and implications of the disinformation ecosystem, and outline a potential research agenda designed to answer these questions.
• More BIC
• The psychology of group identity allows us to understand that group identification can be due to factors that have nothing to do with the individual preferences. Strong interdependence and other forms of common individual interest are one sort of favouring condition, but there are many others, such as comembership of some existing social group, sharing a birthday, and the artificial categories of the minimal group paradigm. (pg 150)
• Wherever we may expect group identity we may also expect team reasoning. The effect of team reasoning on behavior is different from that of individualistic reasoning. We have already seen this for Hi-Lo. This has wide implications. It makes the theory of team reasoning a much more powerful explanatory and predictive theory than it would be if it came on line only in games with th3e right kind of common interest. To take just one example, if management brings it about so that the firm’s employees identify with the firm, we may expect for them to team-reason and so to make choices that are not predicted by the standard theories of rational choice. (pg 150)
• As we have seen, the same person passes through many group identities in the flux of life, and even on a single occasion more than one of these identities may be stimulated. So we will need a model of identity in which the probability of a person’s identification is distributed over not just two alternatives-personal self-identity or identity with a fixed group-but, in principle, arbitrarily many. (pg 151)

# Phil 3.6.18

7:00 – 4:00 ASRC MKT

• Endless tweaking of the presentation
• Pinged Sy – Looks like something on Wednesday. Yep his place around 1:30
• More BIC
• The explanatory potential of team reasoning is not confined to pure coordination games like Hi-Lo. Team reasoning is assuredly important for its role in explaining the mystery facts about Hi-Lo; but I think we have stumbled on something bigger than a new theory of behaviour in pure coordination games. The key to endogenous group identification is not identity of interest but common interest giving rise to strong interdependence. There is common interest in Stag Hunts, Battles of the Sexes, bargaining games and even Prisoner’s Dilemmas. Indeed, in any interaction modelable as a ‘mixed motive’ game there is an element of common interest. Moreover, in most of the landmark cases, including the Prisoner’s Dilemma, the common interest is of the kind that creates strong interdependence, and so on the account of chapter 2 creates pressure for group identification. And given group identification, we should expect team reasoning. (pg 144)
• There is a second evolutionary argument in favour of the spontaneous team-reasoning hypothesis. Suppose there are two alternative mental mechanisms that, given common interest, would lead humans to act to further that interest. Other things being equal, the cognitively cheapest reliable mechanism will be favoured by selection. As Sober and Wilson (1998) put it, mechanisms will be selected that score well on availability, reliability and energy efficiency. Team reasoning meets these criteria; more exactly, it does better on them than the alternative heuristics suggested in the game theory and psychology literature for the efficient solution of common-interest games. (pg 146)
•  (pg 149)
• Educational resources from machine learning experts at Google
• We’re working to make AI accessible by providing lessons, tutorials and hands-on exercises for people at all experience levels. Filter the resources below to start learning, building and problem-solving.
• A Structured Response to Misinformation: Defining and Annotating Credibility Indicators in News Articles
• The proliferation of misinformation in online news and its amplification by platforms are a growing concern, leading to numerous efforts to improve the detection of and response to misinformation. Given the variety of approaches, collective agreement on the indicators that signify credible content could allow for greater collaboration and data-sharing across initiatives. In this paper, we present an initial set of indicators for article credibility defined by a diverse coalition of experts. These indicators originate from both within an article’s text as well as from external sources or article metadata. As a proof-of-concept, we present a dataset of 40 articles of varying credibility annotated with our indicators by 6 trained annotators using specialized platforms. We discuss future steps including expanding annotation, broadening the set of indicators, and considering their use by platforms and the public, towards the development of interoperable standards for content credibility.
• Slide deck for above
• Sprint review
• Presented on Talk, CI2018 paper, JuryRoom, and ONR proposal.
• ONR proposal
• Send annotated copy to Wayne, along with the current draft. Basic question is “is this how it should look? Done
• Ask folks at school for format help?

# Phil 3.5.18

7:00 – 6:00 ASRC MKT

• Dead Reckoning: Navigating Content Moderation After “Fake News”
• Authors Robyn Caplan, Lauren Hanson, and Joan Donovan analyze nascent solutions recently proposed by platform corporations, governments, news media industry coalitions, and civil society organizations. Then, the authors explicate potential approaches to containing “fake news” including trust and verification,disrupting economic incentivesde-prioritizing content and banning accounts, as well as limited regulatory approaches.
• ‘The world is best experienced at 18 mph’. The psychological wellbeing effects of cycling in the countryside: an Interpretative Phenomenological Analysis
• Green Exercise (GE) refers to physical activity conducted whilst simultaneously engaging the natural environment. A substantial body of literature has now been accumulated that establishes that carrying out exercise in this way has significantly greater psychological wellbeing benefits than the non-GE equivalent. Hitherto, seldom has consideration been given to the individual meanings that doing GE has. This study, therefore, sought to understand the lived experience of the phenomenon amongst a group of serious male recreational road bicyclists aged between mid-30s and early 50s who routinely rode in the countryside. Eleven bicyclists participated in semi-structured interviews. Data were analysed using Interpretative Phenomenological Analysis. This revealed themes of mastery and uncomplicated joys; my place to escape and rejuvenate; and alone but connected. Findings indicate that green-cycling served to enhance the participants’ sense of wellbeing and in doing so helped them cope with the mental challenges associated with their lives. It is suggested that green-cycling merges the essential qualities of natural surroundings – including its aesthetic, feelings of calm and a chance for exploration – with the potential for physical challenge and, facilitated by modern technology, opportunities for prosocial behaviours. It also identifies how green-cycling may influence self-determined behaviours towards exercise regulation, suggesting more satisfying and enduring exercise experiences.
• Exhibit A:
• More BIC. I think MB is getting at the theory for why there is explore/exploit in populations
• We have progressed towards a plausible explanation of the behavioural fact about Hi-Lo. It is explicable as an outcome of group identification by the players, because this is likely to produce a way of reasoning, team reasoning, that at once yields A. Team reasoning satisfies the conditions for the mode-P reasoning that we concluded in chapter 1 must be operative if people are ever to reason their way to A. It avoids magical thinking. It takes the profile-selection problem by the scruff of the neck. What explains its onset is an agency transformation in the mind of the player; this agency transformation leads naturally to profile-based reasoning and is a natural consequence of self-identification with the player group. (pg 142)
• Hi-Lo induces group identification. A bit more fully: the circumstances of Hi-Lo cause each player to tend to group-identify as a member of the group G whose membership is the player-set and whose goal is the shared payoff. (pg 142)
• If what induces A-choices is a piece of reasoning which is part of our mental constitution, we are likely to have the impression that choosing A is obviously right. Moreover, if the piece of reasoning does not involve a belief that the coplayer is bounded, we will feel that choosing A is obviously right against a player as intelligent as ourselves; that is, our intuitions will be an instance of the judgemental fact. I suspect, too, that if the reasoning schema we use is valid, rather than involving falacy, our intuitions of reality are likely to be more robust. Later I shall argue that team reasoning is indeed nonfallacious. (pg 143)
• I think this is more than “as intelligent as ourselves”, I think this is a position/orientation/velocity case. I find it compelling that people with different POVs regard each other as ‘stupid’
• When framing tendencies are culture-wide, people in whom a certain frame is operative are aware that it may be operative in others; and if its availability is high, those in it think that it is likely to be operative in others. Here the framing tendency is-so goes my claim-universal, and a fortiori it is culture-wide. (pg 144)
• But for the theory of endogenous team reasoning there are two differences between the Hi-Lo case and these other cases of strong interdependence. First, outside Hi-Los there are counterpressures towards individual self-identification and so I-framing of the problem. In my model this comes out as a reduction in the salience of the strong interdependence, or an increase in that of other features. One would expect these pressures to be very strong in games like Prisoner’s Dilemma, and the fact that C rates are in the 40 per cent range rather than the 90 percent range, so far from surprising, is a prediction of the present theory (pg 144)
• This is where MB starts to get to explore/exploit in populations. There are pressueres that drive groups together and apart. And as individuals, our thresholds for group identification varies
• Working on the ONR whitepaper. Moving over to LaTex because MSword makes me want to injure myself.
• For future reference, here’s my basic LaTex setup:
\documentclass[]{article}

\usepackage{latexsym}
\usepackage{graphicx}
\usepackage{mathptmx}
\usepackage{float}
\usepackage[normalem]{ulem}
\usepackage{wrapfig}

%opening
\title{}
\author{Philip Feldman}

\begin{document}

\maketitle

\begin{abstract}

\end{abstract}

\section{}

\newpage

% Bibliography
\bibliographystyle{acm}
\bibliography{ONR_whitepaper_bib}

\end{document}
• Ok, got all the text moved over. Then I need to out the citations back and start of fix content
• Citations are done.
• Fika
• Presentation by Dr. Greg Walsh:
• For the last 10 years, Greg Walsh has focused on design research around participatory and cooperative design. His efforts include high- and low-tech techniques that extend co-design both geographically and temporally. He has led design research with groups like Nickelodeon, Carnegie Hall, the National Park Service, and most recently, National Public Radio. In this talk, Greg will discuss his work around inclusive and equitable participatory design that range from design-centric Minecraft virtual worlds to Baltimore City public libraries.
• Surprise meeting with Wayne.
• Went over slides. Made some tweaks
• Talked about the ONR and Twitter RFPs. Need to send the ONR proposal for some insight, and get another back
• Slide walkthrough with Brian
• More slide tweaks.
• He suggested that I get in contact with Sy, which makes a lot of sense.

# Phil 2.26.18

7:00 – 6:00 ASRC MKT

• Spread of information is dominated by search ranking
• The spreading process was linear because the background search rate is roughly constant day to day for discounts, and any viral element turned out to be quite small.
• Paper
•  BIC
• There are many conceivable team mechanisms apart from simple direction and team reasoning; they differ in the way in which computation is distributed and the pattern of message sending. For example, one agent might compute o* and send instructions to the others. With the exception of team reasoning, these mechanisms involve the communication of information. If they do I shall call them modes of organization or protocols. (pg 125)
• A mechanism is a general process. The idea (which I here leave only roughly stated) is of a causal process which determines (wholly or partly) what the agents do in any simple coordination context. It will be seen that all the examples I have mentioned are of this kind; contrast a mechanism that applies, say, only in two-person cases, or only to matching games, or only in business affairs. In particular, team reasoning is this kind of thing. It applies to any simple coordination context whatsoever. It is a mode of reasoning rather than an argument specific to a context. (pg 126)
•  Presentation:
• I need to put together a 2×2 payoff matrix that covers nomad/flock/stampede
• Some more heat map views, showing nomad, flocking
• De-uglify JuryRoom
• Timeline of references
• Collapse a few pages 22.5 minutes for presentation and questions
• Work on getting SheetToMap in a swing app? Less figuring things out…
• Slower going than I hoped, but mostly working now. As always, StackOverflow to the rescue: How to draw graph inside swing with GraphStream actually?
• Adding load and save menu choices. Done! Had a few issues with getting the position of the nodes saved out. It seems like you should do this?
GraphicNode gn = viewer.getGraphicGraph().getNode(name);
row.createCell(cellIndex++).setCellValue(gn.getX());
row.createCell(cellIndex++).setCellValue(gn.getY());
• Anyway, pretty pix:
• Start on white paper
• Fika

# Phil 2.25.18

Looks like I need to update the DC and the CI 2018 paper with a new reference:

Dynamic Word Embeddings for Evolving Semantic Discovery

• Zijun YaoYifan Sun, Weicong Ding, Nikhil RaoHui Xiong
• Word evolution refers to the changing meanings and associations of words throughout time, as a byproduct of human language evolution. By studying word evolution, we can infer social trends and language constructs over different periods of human history. However, traditional techniques such as word representation learning do not adequately capture the evolving language structure and vocabulary. In this paper, we develop a dynamic statistical model to learn time-aware word vector representation. We propose a model that simultaneously learns time-aware embeddings and solves the resulting “alignment problem”. This model is trained on a crawled NYTimes dataset. Additionally, we develop multiple intuitive evaluation strategies of temporal word embeddings. Our qualitative and quantitative tests indicate that our method not only reliably captures this evolution over time, but also consistently outperforms state-of-the-art temporal embedding approaches on both semantic accuracy and alignment quality.

# Phil 2.21.18

7:00 – 6:00 ASRC MKT

• Wow – I’m going to the Tensorflow Summit! Need to get a hotel.
• Dimension reduction + velocity in this thread
• Global Pose Estimation with an Attention-based Recurrent Network
• The ability for an agent to localize itself within an environment is crucial for many real-world applications. For unknown environments, Simultaneous Localization and Mapping (SLAM) enables incremental and concurrent building of and localizing within a map. We present a new, differentiable architecture, Neural Graph Optimizer, progressing towards a complete neural network solution for SLAM by designing a system composed of a local pose estimation model, a novel pose selection module, and a novel graph optimization process. The entire architecture is trained in an end-to-end fashion, enabling the network to automatically learn domain-specific features relevant to the visual odometry and avoid the involved process of feature engineering. We demonstrate the effectiveness of our system on a simulated 2D maze and the 3D ViZ-Doom environment.
•  Slides
• Location
• Orientation
• Velocity
• IR context -> Sociocultural context
• Writing Fika. Make a few printouts of the abstract
• It kinda happened. W
• Write up LMN4A2P thoughts. Took the following and put them in a LMN4A2P roadmap document in Google Docs
• Storing a corpora (raw text, BoW, TF-IDF, Matrix)
• Corpora labeling and exploring
• Index with ElasticSearch
• Production of word vectors or ‘effigy documents’
• Effigy search using Google CSE for public documents that are similar
• General
• Site-specific
• Search page
• Lists (reweightable) or terms and documents
• Cluster-based map (pan/zoom/search)
• I’m as enthusiastic about the future of AI as (almost) anyone, but I would estimate I’ve created 1000X more value from careful manual analysis of a few high quality data sets than I have from all the fancy ML models I’ve trained combined. (Thread by Sean Taylor on Twitter, 8:33 Feb 19, 2018)
• Prophet is a procedure for forecasting time series data. It is based on an additive model where non-linear trends are fit with yearly and weekly seasonality, plus holidays. It works best with daily periodicity data with at least one year of historical data. Prophet is robust to missing data, shifts in the trend, and large outliers.
• Done with Angular fundamentals. reDirectTo isn’t working though…
• zone.js:405 Unhandled Promise rejection: Invalid configuration of route '': redirectTo and component cannot be used together ; Zone: <root> ; Task: Promise.then ; Value: Error: Invalid configuration of route '': redirectTo and component cannot be used together

# Phil 2.19.18

7:30 – 4:30 ASRC MKT

• Back to BIC.
•  (page 102)
•  (pg 107)
•  (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 2.17.18

Snow today

Random thought. Marches help because they literally put people in the same position, align them in a direction and enforce a common velocity.

Found this item about compromised Trump company servers. I think this could be framed as an example of excessive trust bringing high social inertia. This would be different from explicit command and control.

So why would the organization allow this? If it’s not fear, then it’s trust. Kramer describes trust arising from incremental and repeated exchanges. I’d like to extend that thought. These incremental and repeated exchanges need to happen in dimension-reduced spaces that are similar or appear to map to each other, and occur with respect to orientation alignment, position, and velocity. The more alignment in these three axis, the greater the trust, and the lower the perceived need for awareness outside the relationship.

Twitter is just full of this stuff today: How A Russian Troll Fooled America

• Reconstructing the life of a covert Kremlin influence account (TEN_GOP)
• The Atlantic Council’s Digital Forensic Research Lab (DFRLab) has operationalized the study of disinformation by exposing falsehoods and fake news, documenting human rights abuses, and building digital resilience worldwide.

# Phil 2.16.18

7:00 – 3:00 ASRC MKT

• Finished the first draft of the CI 2018 extended abstract!
• And I also figured out how to run the sub projects in the Ultimate Angular src collection. You need to go to the root directory for the chapter, run yarn install, then yarn start. Everything works then.
• Trolls on Twitter: How Mainstream and Local News Outlets Were Used to Drive a Polarized News Agenda
• This is the kind of data that compels us to rethink how we understand Twitter — and what I feel are more influential platforms for reaching regular people that include Facebook, Instagram, Google, and Tumblr, as well as understand ad tech tracking and RSS feedharvesting as part of the greater propaganda ecosystem.
• NELA News credibility classification toolkit
• The News Landscape (NELA) Toolkit is an open source toolkit for the systematic exploration of the news landscape. The goal of NELA is to both speed up human fact-checking efforts and increase the understanding of online news as a whole. NELA is made up of multiple indepedent modules, that work at article level granularity: reliability prediction, political impartiality prediction, text objectivity prediction, and reddit community interest prediction. As well as, modules that work at source level granularity: reliability prediction, political impartiality prediction, content-based feature visualization.
• New benchmarks for approximate nearest neighbors
• I built ANN-benchmarksto address this. It pits a bunch of implementations (including Annoy) against each other in a death match: which one can return the most accurate nearest neighbors in the fastest time possible. It’s not a new project, but I haven’t actively worked on it for a while.
• Systems of Global Governance in the Era of Human-Machine Convergence
• Technology is increasingly shaping our social structures and is becoming a driving force in altering human biology. Besides, human activities already proved to have a significant impact on the Earth system which in turn generates complex feedback loops between social and ecological systems. Furthermore, since our species evolved relatively fast from small groups of hunter-gatherers to large and technology-intensive urban agglomerations, it is not a surprise that the major institutions of human society are no longer fit to cope with the present complexity. In this note we draw foundational parallelisms between neurophysiological systems and ICT-enabled social systems, discussing how frameworks rooted in biology and physics could provide heuristic value in the design of evolutionary systems relevant to politics and economics. In this regard we highlight how the governance of emerging technology (i.e. nanotechnology, biotechnology, information technology, and cognitive science), and the one of climate change both presently confront us with a number of connected challenges. In particular: historically high level of inequality; the co-existence of growing multipolar cultural systems in an unprecedentedly connected world; the unlikely reaching of the institutional agreements required to deviate abnormal trajectories of development. We argue that wise general solutions to such interrelated issues should embed the deep understanding of how to elicit mutual incentives in the socio-economic subsystems of Earth system in order to jointly concur to a global utility function (e.g. avoiding the reach of planetary boundaries and widespread social unrest). We leave some open questions on how techno-social systems can effectively learn and adapt with respect to our understanding of geopolitical complexity.