As readers know, I alternate reading based on need and interest.
Felleisen, Findler, Flatt, and Krishnamurthi, How to Design Programs
Abelson, Sussman, and Sussman, Structure and Interpretation of Computer Programs
National Academy of Sciences, Behavioral Modeling and Simulation: From Individuals to Societies
Luger, Stubbenfield, AI Algorithms, Data Structures, and Idioms: In Prolog, Java, and Lisp.
These are reads for schoolwork. The NAC compilation is assigned and the rest are self-reading I am doing to get better at functional programming and program design, and the specific AI programming style.
Some interesting comments by Alex Hanna here.
Her second question:
"What was going on in the none category? If not for mobilization, how else were the members in this Facebook group using this platform? Hanna clearly could not address everything in the limited space in this article, but I hope his future articles will unpack the ambiguity in this none category and suggest some conclusions about what the users were saying in these posts.”
Something which I didn’t explore in the article but I did in several earlier drafts of the master’s from which it was drawn was the idea of discussions around issues, the idea drawing from the well-worn path of the social movement framing perspective. But it was harder to discern whether several unified claims were emerging. I think it’d be a worthwhile project for folks to explore these kinds of data for emergent claims. This may be one place where topic modeling is a more adequate tool for the job.
I appreciate Nelson’s review and hope to see more machine learning used to address existing questions in the social movement literature.
Besides me ( :) ), you should check out some pieces by Colin S. Gray and Brett Friedman.
Second, many scholars appear to be resistant to the conceptually, perhaps even morally, practical recognition of the implications of fact that all ‘policy’ is made by political process, and that that process, everywhere and in all periods, is run and dominated by the people who succeed in being relatively the most influential over others. The substantive content of policy is made in a process of political negotiation among the people and organizations who contend for power, as they must. Decisions on national defence are taken politically, usually with input from subject-specific experts and interests. But, in all systems of governance politics ultimately rules. Prudent assessment concerning the maintenance of their preeminent popular influence flags to political leaders where the limits of the politically tolerable most probably lie. This is not to be critical, it is simply to recognize that we humans run our affairs, including our security affairs, by the means of a political process that is geared to generate power as influence, not prudent policy. Policy does not emerge, pristine and unsullied by unduly subjective emotions, as the ever dynamic product of objective expert analysis.[xviii] This is not to claim that political process will be indifferent to argument with evidence of apparent national danger. But it is to say that strategic theorists and defence analysts (like this author) need to appreciate the humbling professional truth that their contribution to debate on public policy always can be trumped by politics.
Worth reading solely for that tidbit. This is the real Clausewitzian approach to understanding the policy-strategy distinction and the truth behind the “politics by other means” statement.
Conventional armies and guerrilla organizations have no monopoly on any one tactic or set of tactics, even combined arms. A complex attack that combines an IED strike on an entry control point followed up by infiltration attacks is a form of combined arms. The principles of warfare and technological change drive soldier and guerrilla alike towards the same tactical adaptations. For example, the devastating capability of field artillery and close air support since the early 20th century has made large unit concentrations more and more dangerous. In fact, they are nearly suicidal. Ideas of front lines and set-piece battles still ensconced in doctrine are vestigial. Soldiers may enjoy more training than the guerrilla before they arrive on the battlefield, but the guerrilla’s deficit is quickly reduced in the harshest of schools. The only tangible differences between conventional and non-conventional organizations are uniforms, codified regulations, and official designation. History shows that such pomp and circumstance is no guarantor of success in battle. Speaking tactically, there is no “guerrilla” system or “conventional” system. There is simply good tactics, and the guerrilla and the soldier can become equally adept at them. In other words, “irregular” always refers to actors, not the tactics that actors utilize. When combatants as varied as the Lashkar-e-Taiba[viii] in Mumbai, the US Army and Marine Corps, and developing armies like the Somali National Army[ix] are coming to the same conclusions and utilizing the same tactics in vastly different contexts, the idea that a true guerrilla/soldier dichotomy exists falls apart.
This is a great piece, and I’d be interested in seeing if Friedman could go beyond Biddle and perhaps even in future work look at guerrilla uses of Archer Jones’ four basic weapons systems combinations.
Kenneth De Jong, Evolutionary Computation: A Unified Approach
Claudio Cioffi-Revilla, Introduction to Computational Social Science
Michael Burleigh, Small Wars, Faraway Places: Global Insurrection and the Making of the Modern World
Paul Graham, ANSI Common Lisp
E.O. Wilson, On Human Nature
Robin Dunbar, The Human Story
"These amazing simulations end up sounding even better than the real thing" —Gotye, "State of the Art"
Gotye, I appreciate your attempt at interdisciplinary. But despite my own vulnerability to legitimizing “stay in your lane” statements, I’ll offer this as a response. You should focus on making non-crappy music first before offering me insights about modeling and simulation that Derrida said first and better.
I spent a good deal of time clenching my teeth when your one hit came on the radio.
Consider the following questions:
A leader needs to form a coalition in order to ensure security against a powerful adversary. Given a set of potential allies, what are the possible combinations that might produce successful, winning coalitions?
A person involved in a disaster faces a set of competing priorities (safety, family, shelter, neighbors, supplies), which can induce severe frustration, compounded by fear and uncertainty. Which course of action is best, or at least satisfactory?
A country affected by climate change must choose from among a set of competing policies, finite resources, and imperfect information. How can policy analysts arrive at defensible recommendations for policy-makers?
Questions such as these require complex social computations, not just in terms of crude costs and benefits, but also in probabilistic assessments, alternative combinatorial arrangements, fitness assessments with respect to known empirical patterns, and other computational features. The necessary science (social or natural) may also be incomplete, so allowance must be made for deep uncertainty—not just risk with known probability distributions. And yet, as scientists we wish to obtain computable answers to questions such as the three listed above.
From 63-64 of Claudio Cioffi-Revilla, Introduction to Computational Social Science, 2014.
For making better agent-based models (and other similar simulations) using evolutionary computation. Found it in lit review.
I take a more charitable view of this book than my friend tdaxp. The main flaw in the book is, as tdaxp notes, the dated view of psychology:
Indeed, Simon’s discussion of psychology is so dangerously wrong-headed I will spend a paragraph here refuting it. Simon describes the human memory system, and describes two systems long-term memory (which he is generally accurate about) and “short term memory” (which appears to be a confused mix of working memory, associated with general intelligence, and sensory memory, which provides the awareness of taste, etc). In mainstream psychology, long-term-memory and working-term memory as associated with the automatic, highly parallel, intuitive, and effortless “System 1″ cognition system, and the manual, serial, logical, and painfully slow “System 2″ cognition system. In academia these two systems are often studied under “dual process theory,” and in the military they are described as part of the OODA loop.”
Still, as long as Simon isn’t your last reference about psychology it is worth a read. The philosophical elements about the “sciences of the artificial,” “hierarchal complexity,” and the problems of bounded rationality and planning are still pathbreaking in 2013. In particular, the chapter on planning deserves a read from military and security thinkers.
The book is well-written, and some pre-existing knowledge of economics and an tolerance for discrete and combinatorial mathematics is required. Perhaps the only major flaw, besides the obvious psychological issues tdaxp mentions, is the dismissive and dated treatment of chaos theory. Simon was writing at the time in which nonlinear dynamical systems and chaos was beginning to be pioneered by Lorenz, Mandelbrot, Feigenbaum, and others. He doesn’t really engage as much with this, and instead uses catastrophe theory — a loose family member — as a punching bag.
Simon’s work is perhaps most significant as the point in which economics, computer science, cognitive science and artificial intelligence were briefly put together. Economics failed to incorporate Simon’s criticisms and moved forward without it, while more cognitively and computationally-based economics became a heterodox subset.