CiteULike: mattjb's library [991 articles]

Influence diagrams with multiple objectives and tradeoff analysis

M Diehl — 2008-06-25T05:40:09-00:00

Systems, Man and Cybernetics, Part A, IEEE Transactions on, Vol. 34, No. 3. (2004), pp. 293-304.

Influence diagrams have been important models for decision problems because of their ability to both model a problem rigorously at its mathematical level and depict its high-level structure graphically. Once the structure and numerical details of an influence diagram have been specified, it can be evaluated to determine the optimal decision policy. However, when evaluating multiple objectives, in the past this determination was based on the assumption that utility functions that commensurate the objectives are available. This paper extends the structure and solution algorithm for influence diagrams to allow for the inclusion of noncommensurate objectives using multiobjective tradeoff analysis instead of utility theory. This eliminates the need to specify any preference information before the influence diagram is solved. The proposed multiobjective-based methodology is also useful for decision makers who either do not want to accept the assumptions of utility theory for a particular problem, or are confronted with a problem in which it is neither practical nor viable to construct a utility function. Additionally, this paper establishes the relationship between multiobjective influence diagrams and multiobjective decision trees. This relationship is important because it allows a decisionmaker to utilize the advantages of both representations. An example problem is presented to introduce both the extended multiobjective influence diagram methodology and the relationship linking multiobjective decision trees to multiobjective influence diagrams.

Science as if situation mattered

M Bitbol — 2008-06-25T01:49:14-00:00

Phenomenology and the Cognitive Sciences (2002), pp. 181-224.

When he formulated the program of neurophenomenology, Francisco Varela suggested a balanced methodological dissolution of the “hard problem” of consciousness. I show that his dissolution is a paradigm which imposes itself onto seemingly opposite views, including materialist approaches. I also point out that Varela's revolutionary epistemological ideas are gaining wider acceptance as a side effect of a recent controversy between hermeneutists and eliminativists. Finally, I emphasize a structural parallel between the science of consciousness and the distinctive features of quantum mechanics. This parallel, together with the former convergences, point towards the common origin of the main puzzles of both quantum mechanics and the philosophy of mind: neglect of the constitutive blindspot of objective knowledge.

Generalization and Scaling in Reinforcement Learning

DH Ackley — 2008-06-25T01:45:42-00:00

Vol. 2 (1990), pp. 550-557.

In associative reinforcement learning, an environment generates input vectors, a learning system generates possible output vectors, and a reinforcement function computes feedback signals from the input-output pairs. The task is to discover and remember input-output pairs that generate rewards. Especially difficult cases occur when rewards are rare, since the expected time for any algorithm can grow exponentially with the size of the problem. Nonetheless, if a reinforcement function possesses...

Insects, Trees, and Climate: The Bioacoustic Ecology of Deforestation and Entomogenic Climate Change

David Dunn — 2008-06-25T01:14:12-00:00

(11 Dec 2006)

Accumulating observational evidence suggests an intimate connection between rapidly expanding insect populations, deforestation, and global climate change. We review the evidence, emphasizing the vulnerability of key planetary carbon pools, especially the Earth's forests that link the micro-ecology of insect infestation to climate. We survey current research regimes and insect control strategies, concluding that at present they are insufficient to cope with the problem's present regional scale and its likely future global scale. We propose novel bioacoustic interactions between insects and trees as key drivers of infestation population dynamics and the resulting wide-scale deforestation. The bioacoustic mechanisms suggest new, nontoxic control interventions and detection strategies.

On the logical relationship between natural selection and self-organization

GA Hoelzer — 2006-10-16T04:56:07-00:00

Journal of Evolutionary Biology, Vol. 19, No. 6. (November 2006), pp. 1785-1794.

The Cognitive Psychology of Missed Diagnoses

DA Redelmeier — 2008-06-25T00:26:43-00:00

Annals of Internal Medicine, Vol. 142, No. 2. (January 2005), pp. 115-120.

Cognitive psychology is the science that examines how people reason, formulate judgments, and make decisions. This case involves a patient given a diagnosis of pharyngitis, whose ultimate diagnosis of osteomyelitis was missed through a series of cognitive shortcuts. These errors include the availability heuristic (in which people judge likelihood by how easily examples spring to mind), the anchoring heuristic (in which people stick with initial impressions), framing effects (in which people make different decisions depending on how information is presented), blind obedience (in which people stop thinking when confronted with authority), and premature closure (in which several alternatives are not pursued). Rather than trying to completely eliminate cognitive shortcuts (which often serve clinicians well), becoming aware of common errors might lead to sustained improvement in patient care.

The importance of cognitive errors in diagnosis and strategies to minimize them.

P Croskerry — 2007-08-06T15:11:02-00:00

Acad Med, Vol. 78, No. 8. (August 2003), pp. 775-780.

In the area of patient safety, recent attention has focused on diagnostic error. The reduction of diagnostic error is an important goal because of its associated morbidity and potential preventability. A critical subset of diagnostic errors arises through cognitive errors, especially those associated with failures in perception, failed heuristics, and biases; collectively, these have been referred to as cognitive dispositions to respond (CDRs). Historically, models of decision-making have given insufficient attention to the contribution of such biases, and there has been a prevailing pessimism against improving cognitive performance through debiasing techniques. Recent work has catalogued the major cognitive biases in medicine; the author lists these and describes a number of strategies for reducing them ("cognitive debiasing"). Principle among them is metacognition, a reflective approach to problem solving that involves stepping back from the immediate problem to examine and reflect on the thinking process. Further research effort should be directed at a full and complete description and analysis of CDRs in the context of medicine and the development of techniques for avoiding their associated adverse outcomes. Considerable potential exists for reducing cognitive diagnostic errors with this approach. The author provides an extensive list of CDRs and a list of strategies to reduce diagnostic errors.

Recursive noisy OR- a rule for estimating complex probabilistic interactions

JF Lemmer — 2008-06-23T06:08:40-00:00

IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics, Vol. 34, No. 6. (2004), pp. 2252-2261.

This paper focuses on approaches that address the intractability of knowledge acquisition of conditional probability tables in causal or Bayesian belief networks. We state a rule that we term the “recursive noisy OR” (RNOR) which allows combinations of dependent causes to be entered and later used for estimating the probability of an effect. In the development of this paper, we investigate the axiomatic correctness and semantic meaning of this rule and show that the recursive noisy OR is a generalization of the wellknown noisy OR. We introduce the concept of positive causality and demonstrate its utility in axiomatic correctness of the RNOR. We also introduce concepts describing the ways in which dependent causes can work together as being either “synergistic” or “interfering.” We provide a formalization to quantify these concepts and show that they are preserved by the RNOR. Finally, we present a method for the determination of Conditional Probability Tables from this causal theory.

Empirical Multiscale Networks of Cellular Regulation.

Benjamin de Bivort — 2007-10-25T07:46:10-00:00

PLoS Comput Biol, Vol. 3, No. 10. (19 October 2007)

Grouping genes by similarity of expression across multiple cellular conditions enables the identification of cellular modules. The known functions of genes enable the characterization of the aggregate biological functions of these modules. In this paper, we use a high-throughput approach to identify the effective mutual regulatory interactions between modules composed of mouse genes from the Alliance for Cell Signaling (AfCS) murine B-lymphocyte database which tracks the response of approximately 15,000 genes following chemokine perturbation. This analysis reveals principles of cellular organization that we discuss along four conceptual axes. (1) Regulatory implications: the derived collection of influences between any two modules quantifies intuitive as well as unexpected regulatory interactions. (2) Behavior across scales: trends across global networks of varying resolution (composed of various numbers of modules) reveal principles of assembly of high-level behaviors from smaller components. (3) Temporal behavior: tracking the mutual module influences over different time intervals provides features of regulation dynamics such as duration, persistence, and periodicity. (4) Gene Ontology correspondence: the association of modules to known biological roles of individual genes describes the organization of functions within coexpressed modules of various sizes. We present key specific results in each of these four areas, as well as derive general principles of cellular organization. At the coarsest scale, the entire transcriptional network contains five divisions: two divisions devoted to ATP production/biosynthesis and DNA replication that activate all other divisions, an "extracellular interaction" division that represses all other divisions, and two divisions (proliferation/differentiation and membrane infrastructure) that activate and repress other divisions in specific ways consistent with cell cycle control.

Tracing Power and Influence in Networks: Net-Map as a Tool for Research and Strategic Network Planning

E Schiffer — 2008-06-23T03:42:57-00:00

(June 2008)

Believing that complex problems call for complex solutions and that stakeholders should have a say in policies that concern them, policymakers have strongly promoted the development of forums and organizations made up of many stakeholders to address complex governance issues such as water management. Both developing and developed countries have instituted multistakeholder water governance bodies on local, national, and international levels. However, while the belief is strong that these integrated bodies should improve governance, how and to what extent that actually happens is still unclear, not only because of the complexity of the matter but also due to a lack of appropriate research tools for the analysis of complex governance systems. This paper presents an innovative empirical research tool—Net-Map—developed to better understand multistakeholder governance by gathering in-depth information about governance networks, goals of actors, and their power and influence. Researchers and implementers alike can use Net-Map to collect qualitative and quantitative information in a structured and comparable way. It can be used both as a research tool and as an instrument for organizational development and strategic network planning. A case study on the development of a multistakeholder water governance body in northern Ghana illustrates the application of this research method. The method can be used on many different levels, from the community, to national or even international levels. Net-Map merges characteristics of two existing methods, namely social network analysis and the power mapping tool. Using a participatory approach, interviewees and interviewers together draw a network map of the actors involved in the policy arena and characterize the different kinds of links between the actors. They then add “influence towers,” made of checkers pieces, to transfer the abstract concepts of power and influence into a three-dimensional form. Finally, the interviewee assesses the goal orientation of the different actors (for example, developmental versus environmental or pro versus con a certain intervention). The tool provides an influence network map of the governance situation as well as qualitative and quantitative data about the perceived power and influence of the actors. While the data lend themselves to complex quantitative analysis, this paper mainly focuses on the use of the tool for the purpose of mapping and organizational development. The paper explores how the mapping process itself also stimulates a structured in-depth discussion of crucial issues and ways forward. In Ghana, the method has proven to be interculturally applicable and easy to apply and adapt. Interviewees were excited about their own learning processes throughout the interview. Implicit understanding and concepts were visualized and made explicit so that group members could understand where they agree and differ in their perception of the governance arena.

Emergent Actors in World Politics: How States and Nations Develop and Dissolve

LE Cederman — 2008-06-23T01:21:00-00:00

(1997)

The disappearance and formation of states and nations after the end of the Cold War have proved puzzling to both theorists and policymakers. Lars-Erik Cederman argues that this lack of conceptual preparation stems from two tendencies in conventional theorizing. First, the dominant focus on cohesive nation-states as the only actors of world politics obscures crucial differences between the state and the nation. Second, traditional theory usually treats these units as fixed. Cederman offers a fresh way of analyzing world politics: complex adaptive systems modeling. He provides a new series of models--not ones that rely on rational-choice, but rather computerized thought-experiments--that separate the state from the nation and incorporate these as emergent rather than preconceived actors. This theory of the emergent actor shifts attention away from the exclusively behavioral focus of conventional international relations theory toward a truly dynamic perspective that treats the actors of world politics as dependent rather than independent variables. Cederman illustrates that while structural realist predictions about unit-level invariance hold up under certain circumstances, they are heavily dependent on fierce power competition, which can result in unipolarity instead of the balance of power. He provides a thorough examination of the processes of nationalist mobilization and coordination in multi-ethnic states. Cederman states that such states' efforts to instill loyalty in their ethnically diverse populations may backfire, and that, moreover, if the revolutionary movement is culturally split, its identity becomes more inclusive as the power gap in the imperial center's favor increases.

The Role of Occam's Razor in Knowledge Discovery

Pedro Domingos — 2008-06-23T00:52:16-00:00

Data Min. Knowl. Discov., Vol. 3, No. 4. (December 1999), pp. 409-425.

Many KDD systems incorporate an implicit or explicit preference for simpler models, but this use of “Occam’s razor” has been strongly criticized by several authors (e.g., Schaffer, 1993; Webb, 1996). This controversy arises partly because Occam’s razor has been interpreted in two quite different ways. The first interpretation (simplicity is a goal in itself) is essentially correct, but is at heart a preference for more comprehensible models. The second interpretation (simplicity leads to greater accuracy) is much more problematic. A critical review of the theoretical arguments for and against it shows that it is unfounded as a universal principle, and demonstrably false. A review of empirical evidence shows that it also fails as a practical heuristic. This article argues that its continued use in KDD risks causing significant opportunities to be missed, and should therefore be restricted to the comparatively few applications where it is appropriate. The article proposes and reviews the use of domain constraints as an alternative for avoiding overfitting, and examines possible methods for handling the accuracy–comprehensibility trade-off.

Hierarchical Markovian modeling of multi-time scale systems

M Abel — 2008-06-22T23:13:03-00:00

(26 Feb 2002)

We present a systematic way to analyze and model systems having many characteristic time-scales. The method we propose is employed for a test-case of a meandering jet model manifesting chaotic tracer dispersion with long time-correlations. We first choose a suitable state space partition and analyze the symbolic dynamics associated to the fluid particle position. In a second step we construct a stochastic process in terms of a multi-time Markovian model. This corresponds to a hierarchy of random travelers on a graph where each traveler moves at his own time scale. The results are compared on the basis of statistical measures such as entropies and correlation functions.

Bayesian Networks and Influence Diagrams: A Guide to Construction and Analysis

UB Kjærulff — 2008-06-22T22:59:58-00:00

(2008)

* Comprehensive introduction to probabilistic networks * Written specifically for practitioners of applied artificial intelligence * Complete guide to understand, construct, and analyze probabilistic networks Probabilistic networks, also known as Bayesian networks and influence diagrams, have become one of the most promising technologies in the area of applied artificial intelligence, offering intuitive, efficient, and reliable methods for diagnosis, prediction, decision making, classification, troubleshooting, and data mining under uncertainty. Bayesian Networks and Influence Diagrams: A Guide to Construction and Analysis provides a comprehensive guide for practitioners who wish to understand, construct, and analyze intelligent systems for decision support based on probabilistic networks. Intended primarily for practitioners, this book does not require sophisticated mathematical skills or deep understanding of the underlying theory and methods nor does it discuss alternative technologies for reasoning under uncertainty. The theory and methods presented are illustrated through more than 140 examples, and exercises are included for the reader to check his/her level of understanding. The techniques and methods presented for knowledge elicitation, model construction and verification, modeling techniques and tricks, learning models from data, and analyses of models have all been developed and refined on the basis of numerous courses that the authors have held for practitioners worldwide.

A review of accident modelling approaches for complex critical sociotechnical systems

ZH Qureshi — 2008-06-19T03:45:03-00:00

Vol. DSTO-TR-2094 (January 2008)

The increasing complexity in highly technological systems such as aviation, maritime, air traffic control, telecommunications, nuclear power plants, defence and aerospace, chemical and petroleum industry, and healthcare and patient safety is leading to potentially disastrous failure modes and new kinds of safety issues. Traditional accident modelling approaches are not adequate to analyse accidents that occur in modern sociotechnical systems, where accident causation is not the result of an individual component failure or human error. This report provides a review of key traditional accident modelling approaches and their limitations, and describes new system-theoretic approaches to the modelling and analysis of accidents in safety-critical systems. It also discusses current research on the application of formal (mathematically-based) methods to accident modelling and organisational theories on safety and accident causation. This report recommends new approaches to the modelling and analysis of complex systems that are based on systems theory and interdisciplinary research, in order to capture the complexity of modern sociotechnical systems from a broad systemic view for understanding the multi-dimensional aspects of safety and accident causation.

Continuous Time Bayesian Networks

Uri Nodelman — 2008-06-16T05:33:14-00:00

(2007)

Many domains require us to reason about system change. Examples include life history data analysis, financial risk modelling, fault diagnosis, and the study of evolution. Reasoning about such systems involves asking questions about event timing, e.g., when will a person find employment. Our answers, best expressed as probability distributions over time, must account for many factors that are, themselves, changing. Unfortunately, as the number of variables increases, the state space over which we must maintain a distribution grows exponentially. Such exponential growth makes modelling these domains difficult. We introduce the framework of continuous time Bayesian networks (CTBNs) to address this problem. The approach is based on the framework of finite state, homogeneous Markov processes, but uses ideas from Bayesian networks (BNs) to define continuous time models over a structured state space. The CTBN framework uses cyclic graphs that encode conditional independencies in the distribution over the evolution of the system. It explicitly represents temporal dynamics and allows us to query the network for distributions over the times when particular events of interest occur. We specify the class of processes representable by CTBNs and prove there is a unique minimal CTBN structure to encode any representable process. We provide algorithms for learning parameters and structure of CTBN models from both fully observed and partially observed data. We prove that the structure learning problem for CTBNs is easier than for traditional BNs or dynamic Bayesian networks (DBNs). We develop an inference algorithm for CTBNs which is a variant of expectation propagation and leverages domain structure and the explicit model of time for computational advantage. We also show how to use CTBNs to model a rich class of distributions over time. Finally, we compare our framework to DBNs. Importantly, our framework does not require that we choose some fixed temporal granularity; hence, we avoid the DBN requirement that we represent and reason about the process at the finest granularity. Finally, we demonstrate on a real, life history data set that CTBNs with non-exponential duration distributions achieve better performance than DBNs.

Learning Module Networks

Eran Segal — 2008-06-16T03:03:41-00:00

Journal of Machine Learning Research, Vol. 6 (April 2005), pp. 557-588.

Methods for learning Bayesian networks can discover dependency structure between observed variables. Although these methods are useful in many applications, they run into computational and statistical problems in domains that involve a large number of variables. In this paper, we consider a solution that is applicable when many variables have similar behavior. We introduce a new class of models, module networks, that explicitly partition the variables into modules, so that the variables in each module share the same parents in the network and the same conditional probability distribution. We define the semantics of module networks, and describe an algorithm that learns the modules' composition and their dependency structure from data. Evaluation on real data in the domains of gene expression and the stock market shows that module networks generalize better than Bayesian networks, and that the learned module network structure reveals regularities that are obscured in learned Bayesian networks.

Disease Gene Explorer: Display Disease Gene Dependency by Combining Bayesian Networks with Clustering

Qian Diao — 2008-06-16T01:55:07-00:00

(2004), pp. 574-575.

Validating module network learning algorithms using simulated data

Tom Michoel — 2007-05-04T02:30:29-00:00

BMC Bioinformatics, Vol. 8, No. Suppl 2. (2007)

BACKGROUND:In recent years, several authors have used probabilistic graphical models to learn expression modules and their regulatory programs from gene expression data. Despite the demonstrated success of such algorithms in uncovering biologically relevant regulatory relations, further developments in the area are hampered by a lack of tools to compare the performance of alternative module network learning strategies. Here, we demonstrate the use of the synthetic data generator SynTReN for the purpose of testing and comparing module network learning algorithms. We introduce a software package for learning module networks, called LeMoNe, which incorporates a novel strategy for learning regulatory programs. Novelties include the use of a bottom-up Bayesian hierarchical clustering to construct the regulatory programs, and the use of a conditional entropy measure to assign regulators to the regulation program nodes. Using SynTReN data, we test the performance of LeMoNe in a completely controlled situation and assess the effect of the methodological changes we made with respect to an existing software package, namely Genomica. Additionally, we assess the effect of various parameters, such as the size of the data set and the amount of noise, on the inference performance.RESULTS:Overall, application of Genomica and LeMoNe to simulated data sets gave comparable results. However, LeMoNe offers some advantages, one of them being that the learning process is considerably faster for larger data sets. Additionally, we show that the location of the regulators in the LeMoNe regulation programs and their conditional entropy may be used to prioritize regulators for functional validation, and that the combination of the bottom-up clustering strategy with the conditional entropy-based assignment of regulators improves the handling of missing or hidden regulators.CONCLUSION:We show that data simulators such as SynTReN are very well suited for the purpose of developing, testing and improving module network algorithms. We used SynTReN data to develop and test an alternative module network learning strategy, which is incorporated in the software package LeMoNe, and we provide evidence that this alternative strategy has several advantages with respect to existing methods.

Reduction of computational complexity in bayesian networks through removal of weak dependeces

U Kjærulff — 2008-06-16T00:14:59-00:00

(1994)

The paper presents a method for reducing the computational complexity of Bayesian networks through identification and removal of weak dependences (removal of links from the (moralized) independence graph). The removal of a small number of links may reduce the computational complexity dramatically, since several fill-ins and moral links may be rendered superfluous by the removal. The method is described in terms of impact on the independence graph, the junction tree, and the potential functions...

Reasoning at the Right Time Granularity

S Suchi — 2008-06-11T01:20:45-00:00

(2005), pp. 421-430.

Most real-world dynamic systems are composed of different components that often evolve at very different rates. In traditional temporal graphical models, such as dynamic Bayesian networks, time is modeled at a fixed granularity, generally selected based on the rate at which the fastest component evolves. Inference must then be performed at this fastest granularity, potentially at significant computational cost. Continuous Time Bayesian Networks (CTBNs) avoid time-slicing in the representation by modeling the system as evolving continuously over time. The expectation-propagation (EP) inference algorithm of Nodelman et al. (2005) can then vary the inference granularity over time, but the granularity is uniform across all parts of the system, and must be selected in advance. In this paper, we provide a new EP algorithm that utilizes a general cluster graph architecture where clusters contain distributions that can overlap in both space (set of variables) and time. This architecture allows different parts of the system to be modeled at very different time granularities, according to their current rate of evolution. We also provide an information-theoretic criterion for dynamically re-partitioning the clusters during inference to tune the level of approximation to the current rate of evolution. This avoids the need to hand-select the appropriate granularity, and allows the granularity to adapt as information is transmitted across the network. We present experiments demonstrating that this approach can result in significant computational savings.

Strategic Management and Organisational Dynamics

Ralph Stacey — 2008-01-27T11:37:32-00:00

(02 March 2000)

This book argues that in order to succeed in uncertainty and continual change, organizations need to create new perspectives and learn from the chaos within which they operate. This new edition focuses on this radically different approach to strategic management.

Complexity and Creativity in Organizations

Ralph Stacey — 2008-06-10T06:40:39-00:00

(1996)

Multiscale Variety in Complex Systems

Y Bar-Yam — 2007-05-08T04:27:48-00:00

Complexity, Vol. 9, No. 4. (2004), pp. 37-45.

The Law of Requisite Variety is a mathematical theorem relating the number of control states of a system to the number of variations in control that is necessary for effective response. The Law of Requisite Variety does not consider the components of a system and how they must act together to respond effectively. Here we consider the additional requirement of scale of response and the effect of coordinated versus uncoordinated response as a key attribute of complex systems. The components of a system perform a task, with a number of such components needed to act in concert to perform subtasks. We apply the resulting generalization—a Multiscale Law of Requisite Variety—to understanding effective function of complex biological and social systems. This allows us to formalize an understanding of the limitations of hierarchical control structures and the inadequacy of central control and planning in the solution of many complex social problems and the functioning of complex social organizations, e.g., the military, healthcare, and education systems.

Can a biologist fix a radio?--Or, what I learned while studying apoptosis.

Y Lazebnik — 2004-11-22T00:17:30-00:00

Cancer Cell, Vol. 2, No. 3. (September 2002), pp. 179-182.

Colt

W Hoschek — 2008-04-30T04:47:32-00:00

(2004)

Colt provides a set of Open Source Libraries for High Performance Scientific and Technical Computing in Java.

Collective Memory and Spatial Sorting in Animal Groups

Iain Couzin — 2008-02-06T00:05:15-00:00

Journal of Theoretical Biology, Vol. 218, No. 1. (7 September 2002), pp. 1-11.

We present a self-organizing model of group formation in three-dimensional space, and use it to investigate the spatial dynamics of animal groups such as fish schools and bird flocks. We reveal the existence of major group-level behavioural transitions related to minor changes in individual-level interactions. Further, we present the first evidence for collective memory in such animal groups (where the previous history of group structure influences the collective behaviour exhibited as individual interactions change) during the transition of a group from one type of collective behaviour to another. The model is then used to show how differences among individuals influence group structure, and how individuals employing simple, local rules of thumb, can accurately change their spatial position within a group (e.g. to move to the centre, the front, or the periphery) in the absence of information on their current position within the group as a whole. These results are considered in the context of the evolution and ecological importance of animal groups.

Ecological Mechanisms of Evolution by Natural Selection: Causal Processes Generating Density-and-frequency Dependent Fitness

Toshiyuki Nakajima — 2008-04-21T05:54:38-00:00

Journal of Theoretical Biology, Vol. 190, No. 4. (21 February 1998), pp. 313-331.

The current theory of natural selection explains that adaptive evolution occurs because genotypes with greater survival or reproductive tendencies, due to their particular biological properties, tend to increase in frequency over the lesser ones in a common environment; therefore, the former will eventually replace the latter. In nature, such a selection process most often occurs in a local population which is nested in a community involving local ecological dynamics which are not clearly articulated in the explanatory scheme of the theory. This paper seeks to explicate such an ecological process giving rise to the volution of a local population with a particular focus on dynamic effects of an increase in the number of invasive, new types on the fate of old ones. Arguments using the ecological-mechanistic model, representing negative interactions among alternative types of organisms, suggest major ecological mechanisms by which the new replace the old; a selective increase in the number of one type leads to a decrease in the equilibrial abundance of a limiting resource, an increase in the density of conspecifics, and/or an increase in the density of predators, which would in turn lower theper capitareproductive rate, or raise the morality rate of another and make it extinct. Thus, replacement due to selection is associated with such dynamic shifts in equilibria occurring in a local community. The analysis of three (a resource, a prey and a predator) and four species (those plus a top predator) models suggests that evolutionary processes cannot be predicted without reference to the web structure of the community, that some fitness components causing a selective increase in a particular type can have, in some cases, nothing to do with factors causing selective decreases in alternatives, and that evolution of some traits can occur without resource competition.

Niche construction, biological evolution, and cultural change.

KN Laland — 2008-04-21T05:26:10-00:00

The Behavioral and brain sciences, Vol. 23, No. 1. (February 2000)

We propose a conceptual model that maps the causal pathways relating biological evolution to cultural change. It builds on conventional evolutionary theory by placing emphasis on the capacity of organisms to modify sources of natural selection in their environment (niche construction) and by broadening the evolutionary dynamic to incorporate ontogenetic and cultural processes. In this model, phenotypes have a much more active role in evolution than generally conceived. This sheds light on hominid evolution, on the evolution of culture, and on altruism and cooperation. Culture amplifies the capacity of human beings to modify sources of natural selection in their environments to the point where that capacity raises some new questions about the processes of human adaptation.

Complex Limiting Behaviour of Multilocus Genetic Systems in Cyclical Environments

VM Kirzhner — 2008-04-21T05:09:17-00:00

Journal of Theoretical Biology, Vol. 190, No. 3. (7 February 1998), pp. 215-225.

Here we demonstrate that complex limiting behaviour (supercycles and chaotic-like phenomena) may arise in a rather broad and natural class of multilocus systems, both haploid and diploid, experiencing stabilizing selection with cyclically varying optima over a short period. These include loci with purely additive, dominant, or semidominant effects, with different types of their chromosome distribution. The observed complex dynamics appeared to manifest a certain stability with respect to disturbances of parameters specifying the structure of the selected system and environmental characteristics. This mode of multilocus dynamics by far exceeds the potential attainable under ordinary selection models resulting in simple behaviour. It may represent a novel evolutionary mechanism increasing genetic diversity over long time periods. This novel mechanism could contribute to the observation that biological diversity has increased over geological time regardless of the well-known massive extinctions.

Evolution of Transmission Bias in Cultural Inheritance

Kiyosi Takahasi — 2008-04-21T04:55:13-00:00

Journal of Theoretical Biology, Vol. 190, No. 2. (21 January 1998), pp. 147-159.

Evolution of transmission bias in cultural inheritance is investigated using simple models of cultural selection. Conventional models of cultural transmission describe cultural changes by incorporating transmission bias and non-vertical pathways into the ordinary population genetic framework. The methodology has been successful in understanding cultural changes in terms of natural selection, but it is difficult to see from the theoretical framework how biased transmission in favor of maladaptive traits might have evolved. To show that ordinary cultural processes lead at times to the evolution of a preference that favors a deleterious cultural variant, this study presents an alternative model of cultural transmission, where cultural elements are transmitted in a manner more like infections in epidemiological transmission. An ordinary equilibrium analysis indicates that, under certain conditions, runaway dynamics emerges and the coevolution of a maladaptive cultural variant and an associated preference in favor of the maladaptive variant is observed. If the preference of an individual does not change during its ontogeny (e.g., if it is transmitted genetically), however, than cultural selection alone does not produce such runaway dynamics, and only those preferences that favor adaptive variants should eventually evolve. Since cultural processes may at times result in a reduction in the fitness of individuals, simplistic adaptive interpretations of culture are unconvincing without detailed specification of the cultural processes involved. Moreover, cultural runaway of this kind may help to explain the existence of traits that are apparently maladaptive at the individual level but may be advantageous for the group. Inferences are also made regarding the observed differences between human and non-human social information transfer.

Study of Correlations in Segmented DNA Sequences: Application to Structure Coupling between Exons and Introns

VR Chechetkin — 2008-04-21T04:40:25-00:00

Journal of Theoretical Biology, Vol. 190, No. 1. (7 January 1998), pp. 69-83.

A technique for the study of correlations in segmented DNA sequences is developed. Within this approach the effects of compositional patchiness are separated from the beginning, allowing us to display the refined effects of structural coupling between different segments. The mutual analysis of Fourier structure spectra and pair correlation functions identifies both the main ranges (long, short, or intermediate) and the sources (coincident periodicities, large scale density variations, short-memory coupling, or coherent point mutations) of correlations. A scheme is applied to the study of structural coupling between exons and introns in fragmented genes of eukaryotes. The molecular, genetic, and evolutionary aspects of the features observed are discussed.

The Scientist as Philosopher: Philosophical Consequences of Great Scientific Discoveries

Friedel Weinert — 2008-01-08T20:18:57-00:00

(12 January 2005)

How do major scientific discoveries reshape their originators, and our own, sense of reality and concept of the physical world? The Scientist as Philosopher explores the interaction between physics and philosophy. Clearly written and well illustrated, the book first places the scientist-philosophers in the limelight as we learn how their great scientific discoveries forced them to reconsider the time-honored notions with which science had described the natural world. Then, the book explains that what we understand by nature and science have undergone fundamental conceptual changes as a result of the discoveries of electromagnetism, thermodynamics and atomic structure. Even more dramatically, the quantum theory and special theory of relativity questioned traditional assumptions about causation and the passage of time. The author concludes that the dance between science and philosophy is an evolutionary process, which will keep them forever entwined.

Autopoiesis with or without cognition: defining life at its edge

M Bitbol — 2008-03-25T22:46:45-00:00

Journal of The Royal Society Interface, Vol. 1, No. 1. (22 November 2004), pp. 99-107.

This paper examines two questions related to autopoiesis as a theory for minimal life: (i) the relation between autopoiesis and cognition; and (ii) the question as to whether autopoiesis is the necessary and sufficient condition for life. First, we consider the concept of cognition in the spirit of Maturana and Varela: in contradistinction to the representationalistic point of view, cognition is construed as interaction between and mutual definition of a living unit and its environment. The most direct form of cognition for a cell is thus metabolism itself, which necessarily implies exchange with the environment and therefore a simultaneous coming to being for the organism and for the environment. A second level of cognition is recognized in the adaptation of the living unit to new foreign molecules, by way of a change in its metabolic pattern. We draw here an analogy with the ideas developed by Piaget, who recognizes in cognition the two distinct steps of assimilation and accommodation. While assimilation is the equivalent of uptake and exchange of usual metabolites, accommodation corresponds to biological adaptation, which in turn is the basis for evolution. By comparing a micro-organism with a vesicle that uptakes a precursor for its own self-reproduction, we arrive at the conclusion that (a) the very lowest level of cognition is the condition for life, and (b) the lowest level of cognition does not reduce to the lowest level of autopoiesis. As a consequence, autopoiesis alone is only a necessary, but not sufficient, condition for life. The broader consequences of this analysis of cognition for minimal living systems are considered.

Multiple realizability and universality

RW Batterman — 2008-03-25T21:35:49-00:00

Br J Philos Sci, Vol. 51, No. 1. (1 March 2000), pp. 115-145.

This paper concerns what Jerry Fodor calls a 'metaphysical mystery': How can there by macroregularities that are realized by wildly heterogeneous lower level mechanisms? But the answer to this question is not as mysterious as many, including Jaegwon Kim, Ned Block, and Jerry Fodor might think. The multiple realizability of the properties of the special sciences such as psychology is best understood as a kind of universality, where 'universality' is used in the technical sense one finds in the physics literature. It is argued that the same explanatory strategy used by physicists to provide understanding of universal behavior in physics can be used to explain how special science properties can be heterogeneously multiply realized. 10.1093/bjps/51.1.115

A Refinement of the Common Cause Principle

Nihat Ay — 2008-03-25T04:42:55-00:00

(2008)

Abstract. I study the interplay between stochastic dependence and causal relations within the setting of Bayesian networks and in terms of information theory. The application of a recently defined causal information flow measure provides a quantitative refinement of Reichenbach’s common cause principle. Keywords: causality theory; Bayesian networks; information flows; common cause principle; multi-information

The Critical Line in Random Threshold Networks with Inhomogeneous Thresholds

Thimo Rohlf — 2008-03-25T03:34:17-00:00

(2007)

We calculate analytically the critical connectivity Kc of Random Threshold Networks (RTN) for homogeneous and inhomogeneous thresholds, and confirm the results by numerical simulations. We find a super-linear increase of Kc with the (average) absolute threshold |h|, which approaches Kc(|h|) ! |h|! with ! " 2 for large |h|, and show that this asymptotic scaling is universal for RTN with Poissonian distributed connectivity and threshold distributions with a variance that grows slower than |h|!. Interestingly, we find that inhomogeneous distribution of thresholds leads to increased propagation of perturbations for sparsely connected networks, while for densely connected networks damage is reduced. Further, damage propagation in RTN with in-degree distributions that exhibit a scale-free tail k" in is studied; we find that a decrease of " can lead to a transition from supercritical (chaotic) to subcritical (ordered) dynamics. Last, local correlations between node thresholds and in-degree are introduced. Here, numerical simulations show that even weak (anti-)correlations can lead to a transition from ordered to chaotic dynamics, and vice versa. Interestingly, in this case the annealed approximation fails to predict the dynamical behavior for sparse connectivities ¯K , suggesting that even weak topological correlations can strongly limit its applicability for finite N.

Open Problems in the Spectral Analysis of Evolutionary Dynamics

Lee Altenberg — 2008-03-25T02:31:57-00:00

Frontiers of Evolutionary Computation (2004), pp. 73-102.

The dynamics of evolution can be completely characterized by the spectra of the operators that define the dynamics, under broad classes of selection and genetic operators, in both infinite and finite populations. These classes include frequency-independent selection, uniparental inheritance, and generalized mutation. Several open questions exist regarding these spectra:

On the emergence of complex systems on the basis of the coordination of complex behaviors of their elements: Synchronization and complexity

Fatihcan Atay — 2007-04-29T15:16:02-00:00

Complexity, Vol. 10, No. 1. (2004), pp. 17-22.

No abstract.

Ontological uncertainty and innovation

David Lane — 2008-03-25T01:47:58-00:00

Journal of Evolutionary Economics, Vol. 15, No. 1. (1 March 2005), pp. 3-50.

This paper explores the relationship between uncertainty and innovation. It distinguishes three kinds of uncertainty: truth uncertainty, semantic uncertainty, and ontological uncertainty, the latter of which is particularly important for innovation processes. The paper then develops some implications of ontological uncertainty for innovation processes at three levels of organization, by means of three theories: a narrative theory of action at the level of individual economic actors; the theory of generative relationships at the meso-level of agent interaction; and the theory of scaffolding structures at the macro-level of market systems. These theories are illustrated by means of examples drawn from a prospective study on the emergence of a new market system around a technology for distributed control.

The evolution of technology within a simple computer model

Brian Arthur — 2008-03-18T05:01:13-00:00

Complexity, Vol. 11, No. 5. (2006), pp. 23-31.

Technology - the collection of devices and methods available to human society - evolves by constructing new devices and methods from ones that previously exist, and in turn offering these as possible components - building blocks - for the construction of further new devices and elements. The collective of technology in this way forms a network of elements where novel elements are created from existing ones and where more complicated elements evolve from simpler ones. We model this evolution within a simple artificial system on the computer. The elements in our system are logic circuits. New elements are formed by combination from simpler existing elements (circuits), and if a novel combination satisfies one of a set of needs, it is retained as a building block for further combination. We study the properties of the resulting build out. We find that our artificial system can create complicated technologies (circuits), but only by first creating simpler ones as building blocks. Our results mirror Lenski et al.'s: that complex features can be created in biological evolution only if simpler functions are first favored and act as stepping stones. We also find evidence that the resulting collection of technologies exists at self-organized criticality. © 2006 Wiley Periodicals, Inc. Complexity 11: 23-31, 2006

The Origins of Virtue: Human Instincts and the Evolution of Cooperation

Matt Ridley — 2008-03-18T02:33:27-00:00

(01 April 1998)

Human life, scientific journalist Matt Ridley suggests, is a complex balancing act: we behave with self-interest foremost in mind, but also in ways that do not harm, and sometimes even benefit, others. This behavior, in a strange way, makes us good. It also makes us unique in the animal world, where self-interest is far more pronounced. "The essential virtuousness of human beings is proved not by parallels in the animal kingdom, but by the very lack of convincing animal parallels," Ridley writes. How we got to be so virtuous over millions of years of evolution is the theme of this entertaining book of popular science, which will be of interest to any student of human nature. If, as Darwin suggests, evolution relentlessly encourages the survival of the fittest, why are humans compelled to live in cooperative, complex societies? In this fascinating examination of the roots of human trust and virtue, a zoologist and former American editor of the Economist reveals the results of recent studies that suggest that self-interest and mutual aid are not at all incompatible. In fact, he points out, our cooperative instincts may have evolved as part of mankind's natural selfish behavior--by exchanging favors we can benefit ourselves as well as others. Brilliantly orchestrating the newest findings of geneticists, psychologists, and anthropologists, The Origins of Virtue re-examines the everyday assumptions upon which we base our actions towards others, whether in our roles as parents, siblings, or trade partners. With the wit and brilliance of The Red Queen, his acclaimed study of human and animal sexuality, Matt Ridley shows us how breakthroughs in computer programming, microbiology, and economics have given us a new perspective on how and why we relate to each other. • Ridley's previous book, The Red Queen, was short-listed for the Writers' Guild Award for nonfiction.

A model of the ethylene signaling pathway and its gene response in Arabidopsis thaliana: Pathway cross-talk and noise-filtering properties

J Díaz — 2008-03-18T00:33:03-00:00

Chaos, Vol. 16 (2006), 023112.

Dynamic models of molecular networks and pathways enable in silico evaluations of the consistency of proposed interactions and the outcomes of perturbations as well as of hypotheses on system-level structure and function. We postulate a continuous model of the activation dynamics of the ethylene response factor 1 (ERF1) gene in response to ethylene signaling. This activation elicits the response of the plant defensin 1 (PDF1) gene, which also responds to jasmonic acid, and the inhibition of the putative auxin responsive factor 2 (ARF2) gene, that also responds to auxin. Our model allows the effect of different ethylene concentrations in eliciting contrasting genetic and phenotypic responses to be evaluated allows the effect of different ethylene concentrations in eliciting contrasting genetic and phenotypic responses to be evaluated and seems to consider key components of the ethylene pathway because the ERF1 dose-response curve that we predict has the same qualitative form as the phenotypic dose-response curves obtained experimentally. Therefore, our model suggests that the phenotypic dose-response curves obtained experimentally could be due, at least in part, to ERF1 changes to different ethylene concentrations. Stability analyses show that the model's results are robust to parameter estimates. Of interest is that our model predicts that the ethylene pathway may filter stochastic and rapid chaotic fluctuations in ethylene availability. This novel approach may be applied to any cellular signaling and response pathway in plants and animals.

Population and Warfare: A Test of the Turchin Model in Puebloan Societies

TA Kohler — 2008-03-18T00:05:12-00:00

(2006)

Ecologist Peter Turchin and anthropologist Andrey Korotayev (2006) propose that pre-state societies exhibit a deterministic relationship between population size and incidence of internal warfare or sociopolitical instability. We examine their model with data from Southwest Colorado between A.D. 600 and 1300 and find that it fits well during those periods when this area is a more or less closed system. It fits poorly during the time from about A.D. 1000-1200 when this area is heavily influenced first by the spread of the Chacoan system, and then, by its collapse and the local political reorganization that follows. The model is helpful in isolating periods in which the relationship between violence and population size is not as expected. The mechanisms by which it achieves its success need to be elaborated, a task we begin here.

Unanimity rule on networks

R Lambiotte — 2008-03-17T23:38:07-00:00

Phys. Rev. E, Vol. 76, No. 4. (October 2007), 046101.

We present a model for innovation, evolution, and opinion dynamics whose spreading is dictated by a unanimity rule. The underlying structure is a directed network, the state of a node is either activated or inactivated. An inactivated node will change only if all of its incoming links come from nodes that are activated, while an activated node will remain activated forever. It is shown that a transition takes place depending on the initial condition of the problem. In particular, a critical number of initially activated nodes is necessary for the whole system to get activated in the long-time limit. The influence of the degree distribution of the nodes is naturally taken into account. For simple network topologies we solve the model analytically; the cases of random and small world are studied in detail. Applications for food-chain dynamics and viral marketing are discussed.

Spontaneous emergence of modularity in cellular networks

Ricard Solé — 2008-03-17T23:24:13-00:00

Journal of The Royal Society Interface, Vol. 5, No. 18. (6 January 2008), pp. 129-133.

Modularity is known to be one of the most relevant characteristics of biological systems and appears to be present at multiple scales. Given its adaptive potential, it is often assumed to be the target of selective pressures. Under such interpretation, selection would be actively favouring the formation of modular structures, which would specialize in different functions. Here we show that, within the context of cellular networks, no such selection pressure is needed to obtain modularity. Instead, the intrinsic dynamics of network growth by duplication and diversification is able to generate it for free and explain the statistical features exhibited by small subgraphs. The implications for the evolution and evolvability of both biological and technological systems are discussed.

Continuous control of chaos by self-controlling feedback

K Pyragas — 2008-03-12T03:06:27-00:00

Physics Letters A, Vol. 170 (November 1992), pp. 421-428.

<A HREF="/cgi-bin/nph-data_query?link_type=EJOURNAL&bibcode=1992PhLA..170..421P">Electronic Article Available</A> from <A HREF="http://www.elsevier.com">Elsevier Science.</A>

Towards a physics of evolution: Critical diversity dynamics at the edges of collapse and bursts of diversification

Rudolf Hanel — 2008-03-12T00:47:29-00:00

Physical Review E (Statistical, Nonlinear, and Soft Matter Physics), Vol. 76, No. 3. (2007)

Systems governed by the standard mechanisms of biological or technological evolution are often described by catalytic evolution equations. We study the structure of these equations and find an analogy with classical thermodynamic systems. In particular, we can demonstrate the existence of several distinct phases of evolutionary dynamics: a phase of fast growing diversity, one of stationary, finite diversity, and one of rapidly decaying diversity. While the first two phases have been subject to previous work, here we focus on the destructive aspects—in particular the phase diagram—of evolutionary dynamics. The main message is that within a critical region, massive loss of diversity can be triggered by very small external fluctuations. We further propose a dynamical model of diversity which captures spontaneous creation and destruction processes fully respecting the phase diagrams of evolutionary systems. The emergent time series show rich diversity dynamics, including power laws as observed in actual economical data, e.g., firm bankruptcy data. We believe the present model presents a possibility to cast the famous qualitative picture of Schumpeterian economic evolution, into a quantifiable and testable framework.

A Note on Fundamental, Non-fundamental, and Robust Cycle Bases

K Klemm — 2008-03-12T00:35:06-00:00

(2007)

In many biological systems, robustness is achieved by redundant wiring, and re- flected by the presence of cycles in the graphs connecting the systems’ components. When analyzing such graphs, cyclically robust cycle bases of are of interest since they can be used to generate all cycles of a given 2-connected graph by iteratively adding basis cycles. It is known that strictly fundamental (or Kirchhoff ) bases, i.e., those that can be derived from a spanning tree, are not necessarily cyclically robust. Here we note that, conversely, cyclically robust bases (even of planar graphs) are not necessarily fundamental. Furthermore, we present a class of cubic graphs for which cyclically robust bases can be explicitly constructed.

Coupled Contagion Dynamics of Fear and Disease: Mathematical and Computational Explorations

Joshua Epstein — 2008-03-11T23:53:11-00:00

Social Science Research Network Working Paper Series (19 October 2007)

Working Paper Series