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    <title>Pinboard (Vaguery)</title>
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    <description>recent bookmarks from Vaguery</description>
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      <rdf:Seq>	<rdf:li rdf:resource="https://www.mdpi.com/1099-4300/24/4/472"/>
	<rdf:li rdf:resource="https://www.nature.com/articles/s41586-023-06600-9?ref=longnow.org"/>
	<rdf:li rdf:resource="https://stumblingandmumbling.typepad.com/stumbling_and_mumbling/2019/05/how-inequality-makes-us-poorer.html"/>
	<rdf:li rdf:resource="https://arxiv.org/abs/1910.06985"/>
	<rdf:li rdf:resource="https://arxiv.org/abs/1812.06038"/>
	<rdf:li rdf:resource="https://www.biorxiv.org/content/early/2017/04/13/122457?rss=1"/>
	<rdf:li rdf:resource="https://arxiv.org/abs/1707.08905"/>
	<rdf:li rdf:resource="https://arxiv.org/abs/1704.05143"/>
	<rdf:li rdf:resource="https://arxiv.org/abs/1702.07306"/>
	<rdf:li rdf:resource="http://arxiv.org/abs/1510.01757"/>
	<rdf:li rdf:resource="http://arxiv.org/abs/1503.03377"/>
	<rdf:li rdf:resource="http://arxiv.org/abs/1412.3773"/>
	<rdf:li rdf:resource="http://arxiv.org/abs/1409.1197"/>
	<rdf:li rdf:resource="http://www.aeonmagazine.com/being-human/david-berreby-obesity-era/"/>
	<rdf:li rdf:resource="http://www.stat.columbia.edu/~cook/movabletype/archives/2010/03/causality_and_s.html"/>
	<rdf:li rdf:resource="http://infocult.typepad.com/infocult/2009/08/far.html"/>
	<rdf:li rdf:resource="http://www.common-place.org/vol-09/no-03/cahill/"/>
	<rdf:li rdf:resource="http://arxiv.org/abs/math.ST/0609201"/>
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  </channel><item rdf:about="https://www.mdpi.com/1099-4300/24/4/472">
    <title>Naturalising Agent Causation | MDPI</title>
    <dc:date>2025-12-01T17:21:37+00:00</dc:date>
    <link>https://www.mdpi.com/1099-4300/24/4/472</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[The idea of agent causation—that a system such as a living organism can be a cause of things in the world—is often seen as mysterious and deemed to be at odds with the physicalist thesis that is now commonly embraced in science and philosophy. Instead, the causal power of organisms is attributed to mechanistic components within the system or derived from the causal activity at the lowest level of physical description. In either case, the ‘agent’ itself (i.e., the system as a whole) is left out of the picture entirely, and agent causation is explained away. We argue that this is not the right way to think about causation in biology or in systems more generally. We present a framework of eight criteria that we argue, collectively, describe a system that overcomes the challenges concerning agent causality in an entirely naturalistic and non-mysterious way. They are: (1) thermodynamic autonomy, (2) persistence, (3) endogenous activity, (4) holistic integration, (5) low-level indeterminacy, (6) multiple realisability, (7) historicity, (8) agent-level normativity. Each criterion is taken to be dimensional rather than categorical, and thus we conclude with a short discussion on how researchers working on quantifying agency may use this multidimensional framework to situate and guide their research.
]]></description>
<dc:subject>philosophy-of-science agents pragmatism cause-and-effect to-read rather-interesting</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:b7efc58819d8/</dc:identifier>
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	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:pragmatism"/>
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<item rdf:about="https://www.nature.com/articles/s41586-023-06600-9?ref=longnow.org">
    <title>Assembly theory explains and quantifies selection and evolution | Nature</title>
    <dc:date>2025-04-13T19:27:57+00:00</dc:date>
    <link>https://www.nature.com/articles/s41586-023-06600-9?ref=longnow.org</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[Scientists have grappled with reconciling biological evolution1,2 with the immutable laws of the Universe defined by physics. These laws underpin life’s origin, evolution and the development of human culture and technology, yet they do not predict the emergence of these phenomena. Evolutionary theory explains why some things exist and others do not through the lens of selection. To comprehend how diverse, open-ended forms can emerge from physics without an inherent design blueprint, a new approach to understanding and quantifying selection is necessary3,4,5. We present assembly theory (AT) as a framework that does not alter the laws of physics, but redefines the concept of an ‘object’ on which these laws act. AT conceptualizes objects not as point particles, but as entities defined by their possible formation histories. This allows objects to show evidence of selection, within well-defined boundaries of individuals or selected units. We introduce a measure called assembly (A), capturing the degree of causation required to produce a given ensemble of objects. This approach enables us to incorporate novelty generation and selection into the physics of complex objects. It explains how these objects can be characterized through a forward dynamical process considering their assembly. By reimagining the concept of matter within assembly spaces, AT provides a powerful interface between physics and biology. It discloses a new aspect of physics emerging at the chemical scale, whereby history and causal contingency influence what exists.

]]></description>
<dc:subject>assembly-theory self-organization metaheuristics evolutionary-algorithms stochastic-systems rather-interesting exploration-and-exploitation to-understand to-write-about consider:tuning cause-and-effect</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:4c5c085b1e97/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:assembly-theory"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:self-organization"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:metaheuristics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:evolutionary-algorithms"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:stochastic-systems"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:rather-interesting"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:exploration-and-exploitation"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:to-understand"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:to-write-about"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:consider:tuning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:cause-and-effect"/>
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<item rdf:about="https://stumblingandmumbling.typepad.com/stumbling_and_mumbling/2019/05/how-inequality-makes-us-poorer.html">
    <title>Stumbling and Mumbling: How inequality makes us poorer</title>
    <dc:date>2022-01-25T14:16:33+00:00</dc:date>
    <link>https://stumblingandmumbling.typepad.com/stumbling_and_mumbling/2019/05/how-inequality-makes-us-poorer.html</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[1. Inequality encourages the rich to invest not innovation but in what Sam Bowles calls “guard labour” (pdf) – means of entrenching their privilege and power. This might involve restrictive copyright laws, ways of overseeing and controlling workers, or the corporate rent-seeking and lobbying that has led to what Brink Lindsey and Steven Teles call the “captured economy.” An especially costly form of this rent-seeking was banks’ lobbying for a “too big to fail” subsidy. This encouraged over-expansion of the banking system and the subsequent crisis, which has had a massively adverse effect upon economic growth.

]]></description>
<dc:subject>political-economy capitalism inequality cause-and-effect rather-interesting</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:8ac571494534/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:political-economy"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:capitalism"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:inequality"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:cause-and-effect"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:rather-interesting"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/1910.06985">
    <title>[1910.06985] How a minimal learning agent can infer the existence of unobserved variables in a complex environment</title>
    <dc:date>2019-12-15T13:09:24+00:00</dc:date>
    <link>https://arxiv.org/abs/1910.06985</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[According to a mainstream position in contemporary cognitive science and philosophy, the use of abstract compositional concepts is both a necessary and a sufficient condition for the presence of genuine thought. In this article, we show how the ability to develop and utilise abstract conceptual structures can be achieved by a particular kind of learning agents. More specifically, we provide and motivate a concrete operational definition of what it means for these agents to be in possession of abstract concepts, before presenting an explicit example of a minimal architecture that supports this capability. We then proceed to demonstrate how the existence of abstract conceptual structures can be operationally useful in the process of employing previously acquired knowledge in the face of new experiences, thereby vindicating the natural conjecture that the cognitive functions of abstraction and generalisation are closely related. 
Keywords: concept formation, projective simulation, reinforcement learning, transparent artificial intelligence, theory formation, explainable artificial intelligence (XAI)
]]></description>
<dc:subject>machine-learning representation rather-interesting to-understand conceptualization cause-and-effect</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:6dcf3b689da5/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:machine-learning"/>
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	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:rather-interesting"/>
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	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:conceptualization"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:cause-and-effect"/>
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<item rdf:about="https://arxiv.org/abs/1812.06038">
    <title>[1812.06038] Inferring the size of the causal universe: features and fusion of causal attribution networks</title>
    <dc:date>2019-06-03T10:47:59+00:00</dc:date>
    <link>https://arxiv.org/abs/1812.06038</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[Cause-and-effect reasoning, the attribution of effects to causes, is one of the most powerful and unique skills humans possess. Multiple surveys are mapping out causal attributions as networks, but it is unclear how well these efforts can be combined. Further, the total size of the collective causal attribution network held by humans is currently unknown, making it challenging to assess the progress of these surveys. Here we study three causal attribution networks to determine how well they can be combined into a single network. Combining these networks requires dealing with ambiguous nodes, as nodes represent written descriptions of causes and effects and different descriptions may exist for the same concept. We introduce NetFUSES, a method for combining networks with ambiguous nodes. Crucially, treating the different causal attributions networks as independent samples allows us to use their overlap to estimate the total size of the collective causal attribution network. We find that existing surveys capture 5.77% ± 0.781% of the ≈293 000 causes and effects estimated to exist, and 0.198% ± 0.174% of the ≈10 200 000 attributed cause-effect relationships.]]></description>
<dc:subject>statistics explanation cause-and-effect to-understand looking-to-see network-theory</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:e723fa8c33f7/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:explanation"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:cause-and-effect"/>
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	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:network-theory"/>
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<item rdf:about="https://www.biorxiv.org/content/early/2017/04/13/122457?rss=1">
    <title>On reciprocal causation in the evolutionary process | bioRxiv</title>
    <dc:date>2017-10-10T19:59:38+00:00</dc:date>
    <link>https://www.biorxiv.org/content/early/2017/04/13/122457?rss=1</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[Recent calls for a revision the standard evolutionary theory (ST) are based on arguments about the reciprocal causation of evolutionary phenomena. Reciprocal causation means that cause-effect relationships are obscured, as a cause could later become an effect and vice versa. Such dynamic cause-effect relationships raises questions about the distinction between proximate and ultimate causes, as originally formulated by Ernst Mayr. They have also motivated some biologists and philosophers to argue for an Extended Evolutionary Synthesis (EES). Such an EES will supposedly replace the Modern Synthesis (MS), with its claimed focus on unidirectional causation. I critically examine this conjecture by the proponents of the EES, and conclude, on the contrary, that reciprocal causation has long been recognized as important in ST and in the MS tradition. Numerous empirical examples of reciprocal causation in the form of positive and negative feedbacks now exists from both natural and laboratory systems. Reciprocal causation has been explicitly incorporated in mathematical models of coevolutionary arms races, frequency-dependent selection and sexual selection. Such feedbacks were already recognized by Richard Levins and Richard Lewontin, long before the call for an EES and the associated concept of niche construction. Reciprocal causation and feedbacks is therefore one of the few contributions of dialectical thinking and Marxist philosophy in evolutionary theory, and should be recognized as such. While reciprocal causation have helped us to understand many evolutionary processes, I caution against its extension to heredity and directed development if such an extension involves futile attempts to restore Lamarckian or soft inheritance.

]]></description>
<dc:subject>theoretical-biology evolutionary-biology cause-and-effect philosophy-of-science</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:ca61d254b33e/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:theoretical-biology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:evolutionary-biology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:cause-and-effect"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:philosophy-of-science"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/1707.08905">
    <title>[1707.08905] Delegated Causality of Complex Systems</title>
    <dc:date>2017-10-03T11:11:21+00:00</dc:date>
    <link>https://arxiv.org/abs/1707.08905</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[We introduce a simple but subtle, overlooked kind of causality provoked by critical dynamical systems with rich behavior and moderate sensitivity to the environment. By taking clues from Godel's incompleteness theorem, evolutionary biology, Eastern philosophy, we argue that conspicuously complex natural systems build up on interactions of this provoked, delegated causality.
]]></description>
<dc:subject>complexology cause-and-effect philosophy-of-science to-understand emergence</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:bc00778edbd5/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:complexology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:cause-and-effect"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:philosophy-of-science"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:to-understand"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:emergence"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/1704.05143">
    <title>[1704.05143] The Emergence of Canalization and Evolvability in an Open-Ended, Interactive Evolutionary System</title>
    <dc:date>2017-04-28T22:06:12+00:00</dc:date>
    <link>https://arxiv.org/abs/1704.05143</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[Natural evolution has produced a tremendous diversity of functional organisms. Many believe an essential component of this process was the evolution of evolvability, whereby evolution speeds up its ability to innovate by generating a more adaptive pool of offspring. One hypothesized mechanism for evolvability is developmental canalization, wherein certain dimensions of variation become more likely to be traversed and others are prevented from being explored (e.g. offspring tend to have similarly sized legs, and mutations affect the length of both legs, not each leg individually). While ubiquitous in nature, canalization almost never evolves in computational simulations of evolution. Not only does that deprive us of in silico models in which to study the evolution of evolvability, but it also raises the question of which conditions give rise to this form of evolvability. Answering this question would shed light on why such evolvability emerged naturally and could accelerate engineering efforts to harness evolution to solve important engineering challenges. In this paper we reveal a unique system in which canalization did emerge in computational evolution. We document that genomes entrench certain dimensions of variation that were frequently explored during their evolutionary history. The genetic representation of these organisms also evolved to be highly modular and hierarchical, and we show that these organizational properties correlate with increased fitness. Interestingly, the type of computational evolutionary experiment that produced this evolvability was very different from traditional digital evolution in that there was no objective, suggesting that open-ended, divergent evolutionary processes may be necessary for the evolution of evolvability.
]]></description>
<dc:subject>evolutionary-algorithms diversity novelty cause-and-effect rather-interesting to-write-about hey-I-know-this-guy nudge-targets consider:similar-analyses</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:1a80f867fa2b/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:evolutionary-algorithms"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:diversity"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:novelty"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:cause-and-effect"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:rather-interesting"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:to-write-about"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:hey-I-know-this-guy"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:nudge-targets"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:consider:similar-analyses"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/1702.07306">
    <title>[1702.07306] Causal Discovery Using Proxy Variables</title>
    <dc:date>2017-03-05T22:44:16+00:00</dc:date>
    <link>https://arxiv.org/abs/1702.07306</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[Discovering causal relations is fundamental to reasoning and intelligence. In particular, observational causal discovery algorithms estimate the cause-effect relation between two random entities X and Y, given n samples from P(X,Y). 
In this paper, we develop a framework to estimate the cause-effect relation between two static entities x and y: for instance, an art masterpiece x and its fraudulent copy y. To this end, we introduce the notion of proxy variables, which allow the construction of a pair of random entities (A,B) from the pair of static entities (x,y). Then, estimating the cause-effect relation between A and B using an observational causal discovery algorithm leads to an estimation of the cause-effect relation between x and y. For example, our framework detects the causal relation between unprocessed photographs and their modifications, and orders in time a set of shuffled frames from a video. 
As our main case study, we introduce a human-elicited dataset of 10,000 pairs of casually-linked pairs of words from natural language. Our methods discover 75% of these causal relations. Finally, we discuss the role of proxy variables in machine learning, as a general tool to incorporate static knowledge into prediction tasks.
]]></description>
<dc:subject>modeling cause-and-effect inference learning-by-watching rather-interesting to-understand</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:608da0343b9e/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:modeling"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:cause-and-effect"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:inference"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:learning-by-watching"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:rather-interesting"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:to-understand"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/1510.01757">
    <title>[1510.01757] Fuzzy Differences-in-Differences</title>
    <dc:date>2016-03-26T11:24:38+00:00</dc:date>
    <link>http://arxiv.org/abs/1510.01757</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[In many applications of the differences-in-differences (DID) method, the treatment increases more in the treatment group, but some units are also treated in the control group. In such fuzzy designs, a popular estimator of treatment effects is the DID of the outcome divided by the DID of the treatment, or OLS and 2SLS regressions with time and group fixed effects estimating weighted averages of this ratio across groups. We start by showing that when the treatment also increases in the control group, this ratio estimates a causal effect only if treatment effects are homogenous in the two groups. Even when the distribution of treatment is stable, it requires that treatment effects be constant over time. As this assumption is not always applicable, we propose two alternative estimators. The first estimator relies on a generalization of common trends assumptions to fuzzy designs, while the second extends the changes-in-changes estimator of Athey and Imbens (2006). When the distribution of treatment changes in the control group, treatment effects are partially identified. Finally, we prove that our estimators are asymptotically normal and use them to revisit applied papers using fuzzy designs.
]]></description>
<dc:subject>experimental-design statistics modeling-is-not-mathematics fuzzy-math representation cause-and-effect rather-interesting</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:6dd6a12f32b1/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:experimental-design"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:modeling-is-not-mathematics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:fuzzy-math"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:representation"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:cause-and-effect"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:rather-interesting"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/1503.03377">
    <title>[1503.03377] Nonlinearity of local dynamics promotes multi-chimeras</title>
    <dc:date>2015-12-15T15:06:27+00:00</dc:date>
    <link>http://arxiv.org/abs/1503.03377</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[Chimera states are complex spatio-temporal patterns in which domains of synchronous and asynchronous dynamics coexist in coupled systems of oscillators. We examine how the character of the individual elements influences chimera states by studying networks of nonlocally coupled Van der Pol oscillators. Varying the bifurcation parameter of the Van der Pol system, we can interpolate between regular sinusoidal and strongly nonlinear relaxation oscillations, and demonstrate that more pronounced nonlinearity induces multi-chimera states with multiple incoherent domains. We show that the stability regimes for multi-chimera states and the mean phase velocity profiles of the oscillators change significantly as the nonlinearity becomes stronger. Furthermore, we reveal the influence of time delay on chimera patterns.
]]></description>
<dc:subject>coupled-oscillators nonlinear-dynamics rather-interesting cause-and-effect boolean-networks nudge-targets consider:rediscovery</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:7a3b7d53f0f1/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:coupled-oscillators"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:nonlinear-dynamics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:rather-interesting"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:cause-and-effect"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:boolean-networks"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:nudge-targets"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:consider:rediscovery"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/1412.3773">
    <title>[1412.3773] Distinguishing cause from effect using observational data: methods and benchmarks</title>
    <dc:date>2014-12-20T19:49:36+00:00</dc:date>
    <link>http://arxiv.org/abs/1412.3773</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[The discovery of causal relationships from purely observational data is a fundamental problem in science. The most elementary form of such a causal discovery problem is to decide whether X causes Y or, alternatively, Y causes X, given joint observations of two variables X, Y . This was often considered to be impossible. Nevertheless, several approaches for addressing this bivariate causal discovery problem were proposed recently. In this paper, we present the benchmark data set CauseEffectPairs that consists of 88 different "cause-effect pairs" selected from 31 datasets from various domains. We evaluated the performance of several bivariate causal discovery methods on these real-world benchmark data and on artificially simulated data. Our empirical results provide evidence that additive-noise methods are indeed able to distinguish cause from effect using only purely observational data. In addition, we prove consistency of the additive-noise method proposed by Hoyer et al. (2009).
]]></description>
<dc:subject>cause-and-effect causality statistics modeling rather-interesting via:twitter nudge-targets</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:63fa50a7bd7b/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:cause-and-effect"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:causality"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:modeling"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:rather-interesting"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:via:twitter"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:nudge-targets"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/1409.1197">
    <title>[1409.1197] Upper-Level Physics Students' Perceptions of Physicists</title>
    <dc:date>2014-11-07T14:09:18+00:00</dc:date>
    <link>http://arxiv.org/abs/1409.1197</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[As part of a longitudinal study into identity development in upper-level physics students, we used a phenomenographic research method to examine students' perceptions of what it means to be a physicist. The results revealed four different categories. We find a clear distinction in the exclusivity students associate with being a physicist and the differences in the importance of research and its association with being a physicist. A relationship between perceptions of physicists and goal orientation is indicated.
]]></description>
<dc:subject>science-and-society perceptions-of-science physics sociology cause-and-effect</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:dc9f3e43fc98/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:science-and-society"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:perceptions-of-science"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:physics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:sociology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:cause-and-effect"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://www.aeonmagazine.com/being-human/david-berreby-obesity-era/">
    <title>David Berreby – The obesity era</title>
    <dc:date>2013-07-01T11:57:31+00:00</dc:date>
    <link>http://www.aeonmagazine.com/being-human/david-berreby-obesity-era/</link>
    <dc:creator>Vaguery</dc:creator><dc:subject>obesity health science biology cause-and-effect via:adrianh models-and-modes epidemiology</dc:subject>
<dc:identifier>https://pinboard.in/u:Vaguery/b:92c395a881de/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:obesity"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:health"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:science"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:biology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:cause-and-effect"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:via:adrianh"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:models-and-modes"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:epidemiology"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://www.stat.columbia.edu/~cook/movabletype/archives/2010/03/causality_and_s.html">
    <title>Causality and Statistical Learning - Statistical Modeling, Causal Inference, and Social Science</title>
    <dc:date>2010-03-07T17:12:19+00:00</dc:date>
    <link>http://www.stat.columbia.edu/~cook/movabletype/archives/2010/03/causality_and_s.html</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA["The place where I think Sloman is misguided is in his formulation of scientific models in an either/or way, as if, in truth, social variables are linked in simple causal paths, with a scientific goal of figuring out if A causes B or the reverse. I don't know much about intelligence, beer consumption, and socioeconomic status, but I certainly don't see any simple relationships between income, religious attendance, party identification, and voting--and I don't see how a search for such a pattern will advance our understanding, at least given current techniques. I'd rather start with description and then go toward causality following the approach of economists and statisticians by thinking about potential interventions one at a time. I'd love to see Sloman's and Pearl's ideas of the interplay between observational and experimental data developed in a framework that is less strongly tied to the notion of choice among simple causal structures."
]]></description>
<dc:subject>modeling modeling-is-not-mathematics statistics cause-and-effect pragmatism-it-ain't social-sciences scientific-model-fallacies</dc:subject>
<dc:identifier>https://pinboard.in/u:Vaguery/b:715836f0b26d/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:modeling"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:modeling-is-not-mathematics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:cause-and-effect"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:pragmatism-it-ain't"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:social-sciences"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:scientific-model-fallacies"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://infocult.typepad.com/infocult/2009/08/far.html">
    <title>Infocult: Information, Culture, Policy, Education: Yet another study taking down gamers</title>
    <dc:date>2009-09-07T21:48:40+00:00</dc:date>
    <link>http://infocult.typepad.com/infocult/2009/08/far.html</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA["First, note the non-negative aspects of the study.  Stereotypes take another hit as gamers have an average age of 35, and are implicitly equally divided by gender. (Yes, I still get academics telling me gamers are only teen males)  Will these get media attention?

Second, the technological determinism.  Gaming drives depression and bad BMI, it seems, less than games being chosen as art or entertainment by those with such conditions.  One wonders if the social ostracism attached to depression and obesity points one towards a cultural artifact with a bad cultural reputation."
]]></description>
<dc:subject>gaming stereotypes received-wisdom epidemiology cause-and-effect social-norms studies</dc:subject>
<dc:identifier>https://pinboard.in/u:Vaguery/b:ea15969a8695/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:gaming"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:stereotypes"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:received-wisdom"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:epidemiology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:cause-and-effect"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:social-norms"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:studies"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://www.common-place.org/vol-09/no-03/cahill/">
    <title>The Other Panic of 1819</title>
    <dc:date>2009-04-13T14:45:00+00:00</dc:date>
    <link>http://www.common-place.org/vol-09/no-03/cahill/</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA["... Moreover, in order to raise capital and extend their credit over the long, unpredictable term of [an item's] market life, they often endorsed or guaranteed each other's promissory notes, in this way creating elaborate networks of mutual dependence. As a result, when one firm became insolvent, it often took several others down with it. But to make things even worse, many [brokers of these items] estimated their net worth based on unsold (and devalued) inventory rather than on a more realistic accounting of their assets. This meant that, at any given time, it was difficult for a [broker of these items] to know either his own true financial position or that of the firms whose notes he'd endorsed. Thus, by 1819, with many thousands of worthless [items] circulating as inflated currency, the bankruptcy of a [broker of these items] was a frequent occurrence."
]]></description>
<dc:subject>financial-crisis books bookselling this-has-all-happened-before nanohistory history cause-and-effect social-networks economics gales-of-derisive-change</dc:subject>
<dc:identifier>https://pinboard.in/u:Vaguery/b:09180c9dced5/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:financial-crisis"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:books"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:bookselling"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:this-has-all-happened-before"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:nanohistory"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:history"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:cause-and-effect"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:social-networks"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:economics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:gales-of-derisive-change"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/math.ST/0609201">
    <title>[math/0609201] Estimating the Causal Effects of Marketing Interventions Using Propensity Score Methodology</title>
    <dc:date>2007-07-02T15:33:27+00:00</dc:date>
    <link>http://arxiv.org/abs/math.ST/0609201</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[via Cosma Shalizi
]]></description>
<dc:subject>statistics data-analysis models cause-and-effect analytics preprint propensity</dc:subject>
<dc:identifier>https://pinboard.in/u:Vaguery/b:201189620590/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:data-analysis"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:models"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:cause-and-effect"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:analytics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:preprint"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:propensity"/>
</rdf:Bag></taxo:topics>
</item>
</rdf:RDF>