Bound-constrained optimization has wide applications in science and engineering. In the last two decades, various evolutionary algorithms were developed under the umbrella of evolutionary computation for solving various bound-constrained benchmark functions and various real-world problems. In general, the developed evolutionary algorithms belong to nature-inspired algorithms and swarm intelligence paradigms. Differential evolutionary algorithm is one of the most popular and well-known EAs and has secured top ranks in most of the EA competitions in the special session of the (...) IEEE Congress on Evolutionary Computation. In this paper, a customized differential evolutionary algorithm is suggested and applied on twenty-nine large-scale bound-constrained benchmark functions. The suggested C-DE algorithm has obtained promising numerical results in its 51 independent runs of simulations. Most of the 2013 IEEE-CEC benchmark functions are tackled efficiently in terms of proximity and diversity. (shrink)

Constrained optimization plays an important role in many decision-making problems and various real-world applications. In the last two decades, various evolutionary algorithms were developed and still are developing under the umbrella of evolutionary computation. In general, EAs are mainly categorized into nature-inspired and swarm-intelligence- based paradigms. All these developed algorithms have some merits and also demerits. Particle swarm optimization, firefly algorithm, ant colony optimization, and bat algorithm have gained much popularity and they have successfully tackled various (...) test suites of benchmark functions and real-world problems. These SI-based algorithms follow the social and interactive principles to perform their search process while approximating solution for the given problems. In this paper, a multiswarm-intelligence-based algorithm is developed to cope with bound constrained functions. The suggested algorithm integrates the SI-based algorithms to evolve population and handle exploration versus exploitation issues. Thirty bound constrained benchmark functions are used to evaluate the performance of the proposed algorithm. The test suite of benchmark function is recently designed for the special session of EAs competition in IEEE Congress on Evolutionary Computation. The suggested algorithm has approximated promising solutions with good convergence and diversity maintenance for most of the used bound constrained single optimization problems. (shrink)

The constraint-handling methods using multiobjective techniques in evolutionary algorithms have drawn increasing attention from researchers. This paper proposes an efficient conical area differential evolution algorithm, which employs biased decomposition and dual populations for constrained optimization by borrowing the idea of cone decomposition for multiobjective optimization. In this approach, a conical subpopulation and a feasible subpopulation are designed to search for the global feasible optimum, along the Pareto front and the feasible segment, respectively, in a cooperative way. In (...) particular, the conical subpopulation aims to efficiently construct and utilize the Pareto front through a biased cone decomposition strategy and conical area indicator. Neighbors in the conical subpopulation are fully exploited to assist each other to find the global feasible optimum. Afterwards, the feasible subpopulation is ranked and updated according to a tolerance-based rule to heighten its diversity in the early stage of evolution. Experimental results on 24 benchmark test cases reveal that CADE is capable of resolving the constrained optimization problems more efficiently as well as producing solutions that are significantly competitive with other popular approaches. (shrink)

This paper explores an approach for formulating a prescriptive theory of the firm that integrates economic and ethical criteria to guide strategic and operationalconduct of managers. A prescriptive theory posits goal optimization. A “constrained multiple goal optimization” approach models the firm as a broad set of multiple goals and multiple constraints, the latter both internal and external. An exploration begins with no assumptions concerning whether economics and ethics are compatible or antithetical. If the two approaches are mutually (...) reinforcing, a win-win situation obtains. If the two approaches are in win-lose conflict, a key issue is which approach is hierarchically superior or whether the two approaches can be weighted relatively. (shrink)

Let G V, E be a simple undirected complete graph with vertex and edge sets V and E, respectively. In this paper, we consider the degree-constrained k -minimum spanning tree problem which consists of finding a minimum cost subtree of G formed with at least k vertices of V where the degree of each vertex is less than or equal to an integer value d ≤ k − 2. In particular, in this paper, we consider degree values of d (...) ∈ 2,3. Notice that DC k MST generalizes both the classical degree-constrained and k -minimum spanning tree problems simultaneously. In particular, when d = 2, it reduces to a k -Hamiltonian path problem. Application domains where DC k MST can be adapted or directly utilized include backbone network structures in telecommunications, facility location, and transportation networks, to name a few. It is easy to see from the literature that the DC k MST problem has not been studied in depth so far. Thus, our main contributions in this paper can be highlighted as follows. We propose three mixed-integer linear programming models for the DC k MST problem and derive for each one an equivalent counterpart by using the handshaking lemma. Then, we further propose ant colony optimization and variable neighborhood search algorithms. Each proposed ACO and VNS method is also compared with another variant of it which is obtained while embedding a Q-learning strategy. We also propose a pure Q-learning algorithm that is competitive with the ACO ones. Finally, we conduct substantial numerical experiments using benchmark input graph instances from TSPLIB and randomly generated ones with uniform and Euclidean distance costs with up to 400 nodes. Our numerical results indicate that the proposed models and algorithms allow obtaining optimal and near-optimal solutions, respectively. Moreover, we report better solutions than CPLEX for the large-size instances. Ultimately, the empirical evidence shows that the proposed Q-learning strategies can bring considerable improvements. (shrink)

A comprehensive introduction to optimization with a focus on practical algorithms for the design of engineering systems. This book offers a comprehensive introduction to optimization with a focus on practical algorithms. The book approaches optimization from an engineering perspective, where the objective is to design a system that optimizes a set of metrics subject to constraints. Readers will learn about computational approaches for a range of challenges, including searching high-dimensional spaces, handling problems where there are multiple competing (...) objectives, and accommodating uncertainty in the metrics. Figures, examples, and exercises convey the intuition behind the mathematical approaches. The text provides concrete implementations in the Julia programming language. Topics covered include derivatives and their generalization to multiple dimensions; local descent and first- and second-order methods that inform local descent; stochastic methods, which introduce randomness into the optimization process; linear constrained optimization, when both the objective function and the constraints are linear; surrogate models, probabilistic surrogate models, and using probabilistic surrogate models to guide optimization; optimization under uncertainty; uncertainty propagation; expression optimization; and multidisciplinary design optimization. Appendixes offer an introduction to the Julia language, test functions for evaluating algorithm performance, and mathematical concepts used in the derivation and analysis of the optimization methods discussed in the text. The book can be used by advanced undergraduates and graduate students in mathematics, statistics, computer science, any engineering field, (including electrical engineering and aerospace engineering), and operations research, and as a reference for professionals. (shrink)

In this paper, an in-depth study of interactive visual communication of network topology through non-line-of-sight congestion control algorithms is conducted to address the real-time routing problem of adapting to dynamic topologies, and a delay-constrained stochastic routing algorithm is proposed to enable packets to reach GB within the delay threshold in the absence of end-to-end delay information while improving network throughput and reducing network resource consumption. The algorithm requires each sending node to select an available relay set based on the (...) location of its neighbor nodes and channel state and computes transfer probabilities for each node in the relay set combining the remaining delay of the packet with the distance from the relay node to GB. Based on the obtained transfer probability and local channel state, the sending node passes the packet to the relay node. The convergence of the algorithm is proved and its performance is verified by simulation. The first part of the algorithm is based on the greedy algorithm to deploy and locate the network flying platform nodes with the goal of efficient coverage of the network flying platform nodes, considering the ground base station services. As the delay on each link varies due to the change of channel state, the source and relay nodes asynchronously update the data generation rate and the pairwise parameters based on the received local information and use the obtained optimal values to pass the packets to GB. (shrink)

1. Germanic prosody. The early Germanic languages are characterized by fixed initial stress, free quantity, and a preference for moraic trochees, left-headed bimoraic feet consisting either of two light syllables (LL) or of one heavy syllable (H).1 The two-mora foot template places indirect constraints on syllable structure, by making it hard to accommodate three-mora syllables, as well as one-mora syllables in contexts where they cannot join another one-mora syllable to form a two-mora trochee. Syllable structure is also constrained more (...) directly by a preference for simple onsets, which entails an avoidance both of hiatus and of syllable-initial consonant clusters. Processes of syllabification, deletion, shortening and lengthening in the Germanic languages favor those quantitative and syllabic patterns that fit these prosodic conditions, and repair those that do not.2 It is not always possible to satisfy all of the preferences at the same time, however, and so the morphophonology must adjudicate between their conflicting demands. While the preferences themselves are invariant, the languages diverge in how they resolve contradictions between them. Some stretch the prosodic limits by allowing excess segments to be accommodated by overlength or resolution, others delete segments (e.g. glide deletion, high vowel deletion), adjust vowel length to fit the template, or tolerate hiatus. For example, a long ja-stem such as /herdi-/ with a vocalic ending, say /herdi-a/,3 presents a prosodic quandary to which the languages respond with three different compromises: (1) a. Proto-Germanic: *hir. i.a (hiatus) b. Gothic: herd.ja (a three-mora syllable) c. Old Icelandic: hir. a (deletion of j). (shrink)

Under the background of the information age, scientific research and engineering practice have developed vigorously, resulting in many complex optimization problems that are difficult to solve. How to design more effective optimization methods has become the focus of urgent solutions in many academic fields. Under the guidance of such demand, intelligent optimization algorithms have emerged. This article analyzes and optimizes the modern artificial intelligence teaching information system in detail. On the basis of determining the network architecture, a (...) detailed demand analysis was carried out, and the overall structure optimization of the network was given; the business process and data flow of the main modules of the website were optimized. In order to further enhance the local search ability of the algorithm, a multiclass interactive optimization algorithm is proposed in combination with the Euclidean distance-based clustering method, which changes the teaching mode from “one-person teaching” to “multiperson teaching.” This clustering method has lower complexity and is beneficial to enhance the utilization of neighborhood information. At the same time, in order to enhance the diversity of the population and strengthen the connection between the subgroups, after the teaching phase, the worst students in each subgroup are allowed to learn from the best teachers of the population, and after the learning phase, individuals in a random subgroup are allowed to learn from other subgroups. The algorithm was tested in the experimental environment of unconstrained, constrained, and an engineering problem. From the test results, it can be seen that the algorithm is not easy to fall into the local optimum. Compared with other algorithms, the solution accuracy is higher and the stability is better. And it performed well in engineering optimization problems, thus verifying the effectiveness of the strategy. (shrink)

To reduce CO2 emissions requires greater reliance on renewable sources of energy for generating electricity, especially adoption of large-scale wind generation. This study investigates possible approaches and/or policies that increase efficient use of renewable energy and reduce greenhouse gas emissions in a cost effective manner. We develop a constrained optimization model of two electricity systems to identify the impact of increasing wind generating capacity and examine how carbon prices (taxes, allowances) impact the penetration of wind power into the (...) electricity grids. Rather than employ engineering cost functions, marginal cost functions are estimated using hourly offer data from the Alberta Electric System Operator. We determine optimal removal of coal generating facilities as greater levels of wind capacity are installed in an integrated Alberta-BC electricity system; and examine the economic costs and institutional incentives that affect the ability to store intermittent wind-generated power in BC’s hydro reservoirs during low demand. The marginal shadow price of storage is zero, whichindicates that there is more than enough water behind the dams given Alberta’s relatively small demand for storage and limited intertie transmission capacity. (shrink)

Our work is aimed at studying the optimization of a complex motor behaviour from a global perspective. First, free climbing as a sport will be briefly introduced while emphasizing in particular its psychomotor aspect called route finding. The basic question raised here is how does the optimization of a sensorimotoricity-environment system take place. The material under study is the free climber's trajectory, viewed as the signature of climbing behaviour (i.e., the spatial dimension). The concepts of learning, optimization, (...) constraint, and degrees of freedom of a system will be discussed using the synergistic approach to the study of movement (Bernstein, 1967; Kelso, 1977). Measures of a trajectory's length and convex hull can be used to define an index whose equation resembles that of an entropy. This index is a measure of the trajectory's overall complexity. Some important concepts related to the thermodynamics of curves will also be discussed. The optimization process will be studied by examining the changes in entropy over time for a set of trajectories generated during the learning of a route (ten successive repetitions of the same climb). It will be shown that the entropy of the trajectories decreases as learning progresses, that each level of expertise has its own characteristic entropy curve, and that for the subjects tested, the mean entropy of skilled climbers is lower than that of average climbers. Basing our analysis on the concepts of degrees of freedom and constraint equations, an attempt is made to relate trajectory entropy to system entropy. Based on the postulate that trajectory entropy is equal to the difference in entropy between the unconstrained and constrained system, a model of motor optimization is proposed. This model is illustrated by an entropy graph reflecting a dynamic release process. In the light of our results, two opposing views will be examined: movement construction vs. movement emergence. (shrink)

Hybrid renewable energy system arises regularly in real life. By optimizing the capacity and running status of the microgrid, HRES can decrease the running cost and improve the efficiency. Such an optimization problem is generally a constrained mixed-integer programming problem, which is usually solved by linear programming method. However, as more and more devices are added into MG, the mathematical model of HRES refers to nonlinear, in which the traditional method is incapable to solve. To address this issue, (...) we first proposed the mathematical model of an HRES. Then, a coevolutionary multiobjective optimization algorithm, termed CMOEA-c, is proposed to handle the nonlinear part and the constraints. By considering the constraints and the objective values simultaneously, CMOEA-c can easily jump out of the local optimal solution and obtain satisfactory results. Experimental results show that, compared to other state-of-the-art methods, the proposed algorithm is competitive in solving HRES problems. (shrink)

Offshore micro integrated energy systems are the basis of offshore oil and gas engineering and play an important role in developing and utilizing marine resources. By introducing offshore wind power generation, the carbon emissions of offshore micro integrated energy systems can be effectively reduced; however, greater challenges have been posted to the reliable operation due to the uncertainty. To reduce the influence brought by the uncertainty, a multiobjective optimization model was proposed based on the chance-constrained programming ; the (...) operating cost and penalty cost of natural gas emission were selected as objectives. Then, the improved hybrid constraints handling strategy based on nondominated sorting genetic algorithm II was introduced to solve the model efficiently. Finally, the numerical studies verified the efficiency of the proposed algorithm, as well as the validity and feasibility of the proposed model in improving the economy of OMIES under uncertainty. (shrink)

Function optimization underlies many real-world problems and hence is an important research subject. Most of the existing optimization methods were developed to solve primarily unconstrained problems. Since real-world problems are often constrained, appropriate handling of constraints is necessary in order to use the optimization methods. In particular, the performances of some methods such as Genetic Algorithms can be substantially undermined by ineffective constraint handling. Despite much effort devoted to the studies of constraint-handling methods, it has been (...) reported that each of them has certain limitations. Hence, further studies for designing more effective constraint-handling methods are needed. For this reason, we investigated the guidelines for a method to effectively handle constraints. The guidelines are that the method 1) takes the approach of repair operators, 2) monotonically decreases both the number of violated constraints and constraint violations, and 3) searches over the boundaries of violated constraints. Based on these guidelines, we designed a new constraint-handling method Pareto Descent Repair operator in which ideas derived from multi-objective local search and gradient projection method are incorporated. Experiments comparing GA that use PDR and some of the existing constraint-handling methods confirmed the effectiveness of PDR. (shrink)

This book offers a rigorous, concise, and nontechnical introduction to some of the fundamental insights of rational choice theory. It draws on formal theories of microeconomics, decision making, games, and social choice, and on ideas developed in philosophy, psychology, and sociology. Itzhak Gilboa argues that economic theory has provided a set of powerful models and broad insights that have changed the way we think about everyday life. He focuses on basic insights of the rational choice paradigm--the general conceptualization rather than (...) a particular theory--that survive recent critiques of economic theory's various failures. Gilboa explains the main concepts in language accessible to the nonspecialist, offering a nonmathematical guide to some of the main ideas developed in economic theory in the second half of the twentieth century. Chapters cover feasibility and desirability, utility maximization, constrained optimization, expected utility, probability and statistics, aggregation of preferences, games and equilibria, free markets, and rationality and emotions. Online appendixes offer additional material, including a survey of relevant mathematical concepts. (shrink)

Alongside other types of mass atrocities, genocide has received extensive scholarly, policy, and practitioner attention. Missing, however, is the contribution of economists to better understand and prevent such crimes. This edited collection by 41 accomplished scholars examines economic aspects of genocides, other mass atrocities, and their prevention. Chapters include numerous case studies, probing literature reviews, and completely novel work based on extraordinary country-specific datasets. Also included are chapters on the demographic, gendered, and economic class nature of genocide. Replete with research- (...) and policy-relevant findings, new insights are derived from behavioral economics, law and economics, political economy, macroeconomic modeling, microeconomics, development economics, industrial organization, identity economics, and other fields. Analytical approaches include constrained optimization theory, game theory, and sophisticated statistical work in data-mining, econometrics, and forecasting.A foremost finding of the book concerns atrocity architects' purposeful, strategic use of violence, often manipulating nonrational proclivities among ordinary people to sway their participation in mass murder. Relatively understudied in the literature, the book also analyzes the options of victims before, during, and after mass violence. Further, the book shows how well-intended prevention efforts can backfire and increase violence, how wrong post-genocide design can entrench vested interests to reinforce exclusion of vulnerable peoples, and how businesses can become complicit in genocide. In addition to the necessity of healthy opportunities in employment, education, and key sectors in prevention work, the book shows why new genocide prevention laws and institutions must be based on reformulated incentives that consider insights from law and economics, behavioral economics, and collective action economics. (shrink)

Pathological gambling (PG) is a kind of ‘ideal puzzle’ for the economic model of the consumer. The pathological gambler takes pains to engage in activity that transparently has negative expected returns if utility varies positively with money. She also, typically, spends further resources on commitment devices designed to interfere with her gambling. These properties together describe an agent that is a kind of perfect foil for the rationally maximizing consumer. Recently, aspects of the neuropathology underlying the strange economic agency of (...) the pathological gambler are becoming understood. Thus PG is an ideal test bed phenomenon for working out relationships between economic modeling based on constrained optimization of utility and the new neuroscience of behavior. (shrink)

Dialogic leadership as ethics method respects, values, and works toward organizational objectives. However, in those situations where there may be conflicts and/or contradictions between what is ethical and what is in the material interest of individuals and/or the organization, the dialogic leader initiates discussion with others (peers, subordinates, superiors) about what is ethical with at least something of a prior ethics truth intention and not singularly a value neutral, constrained optimization of organizational objectives. Cases are considered where dialogic (...) leadership: (1) helped build ethical organizational culture; (2) was effective; and, (3) as a by-product, produced integrative win-win results. Philosophical foundations for the method as well as differences between dialogic leadership and Theory X forcing leadership, Theory Y win-win integrative leadership, industrial democracy, participative management, action inquiry, and double-loop learning action science are explored. Limitations of the method are also explored. (shrink)

In the eyes of its practitioners, economics is both a deductive science and an empirical science. The starting point of its deductions might be thought of as first principles. But what is the status of such principles? The tension between foundationalism, the idea that there are necessary and secure first principles for economic inquiry, and fallibilism, the idea that no belief can be certified as true beyond the possibility of doubt, is explored. Empirical disciplines require some sort of falsifiability. Yet, (...) empirical inquiries also require a starting place—if not a necessarily true one, at least an indubitable one, that is, one that is not actually doubted. Indubitability appears to have necessary consequences, undercutting fallibilism, while fallibilism threatens confidence in the de facto first principles that begin inquiry. This tension is examined in three well-known attempts to define economics and its method: John Stuart Mill’s economics as the science of wealth, Lionel Robbins’s economics as constrained optimization; and George Stigler and Becker’s attempt to reformulate neoclassical economics to square empiricism with Robbins’ deductivism. (shrink)

Nature-inspired optimization techniques are useful tools in electrical engineering problems to minimize or maximize an objective function. In this paper, we use the firefly algorithm to improve the optimal solution for the problem of directional overcurrent relays (DOCRs). It is a complex and highly nonlinear constrained optimization problem. In this problem, we have two types of design variables, which are variables for plug settings (PSs) and the time dial settings (TDSs) for each relay in the circuit. The (...) objective function is to minimize the total operating time of all the basic relays to avoid unnecessary delays. We have considered four models in this paper which are IEEE (3-bus, 4-bus, 6-bus, and 8-bus) models. From the numerical results, it is obvious that the firefly algorithm with certain parameter settings performs better than the other state-of-the-art algorithms. (shrink)

After the Second World War, Italy experienced an economic miracle accompanied by the emergence of a material culture highly dense with meaning. This article adopts a second-order approach, which focuses on two concepts that emphasize the component of invention contained within the innovation process.Garboindicates the peculiarly Italian way of solving a constrained optimization problem in the design of everyday objects. Meanwhile, the concept ofcenacolo– whose etymological roots indicate conviviality and good living – made possible the study of the (...) peculiar social networks of the Milanese cultural landscape during the 1960s, which enabled important cross-fertilizations between industry, culture and art. To demonstrate the connections between invention andgarboandcenacoli, the examples of Olivetti (key player in then-nascent personal computer technology) and Bialetti (producer of the Moka coffee machine) are used as case studies of innovative solutions to constrained problems. Following an outline of elements promoting the success of each, the article identifies historically determined mechanisms, which enable us to imagine and (potentially) establish the evolutionary conditions for new pathways of invention. (shrink)

This study aimed to explore the association between self-oriented empathy and compassion fatigue, and examine the potential mediating roles of dispositional mindfulness and the counselor’s self-efficacy. A total of 712 hotline psychological counselors were recruited from the Mental Health Service Platform at Central China Normal University, Ministry of Education during the outbreak of Corona Virus Disease 2019, then were asked to complete the questionnaires measuring self-oriented empathy, compassion fatigue, dispositional mindfulness, and counselor’s self-efficacy. Structural equation modeling was utilized to analyze (...) the possible associations and explore potential mediations. In addition to reporting confidence intervals, we employed a new method named model-based constrained optimization procedure to test hypotheses of indirect effects. Results showed that self-oriented empathy was positively associated with compassion fatigue. Dispositional mindfulness and counselor’s self-efficacy independently and serially mediated the associations between self-oriented empathy and compassion fatigue. The findings of this study confirmed and complemented the etiological and the multi-factor model of compassion fatigue. Moreover, the results indicate that it is useful and necessary to add some training for increasing counselor’s self-efficacy in mindfulness-based interventions in order to decrease compassion fatigue. (shrink)

Resumen La tesis principal del artículo es que la estrategia estándar de modelización económica basada en la construcción de sistemas cerrados y de agentes optimizadores es ineficaz para el tratamiento de la incertidumbre. Puesto que bajo incertidumbre los agentes económicos desconocen cuál es el resultado final de un curso de acción, las decisiones de estos no deberían ser representadas por el cálculo de optimización bajo restricciones. La construcción de sistemas axiomáticos deductivos que conducen a la derivación de un único resultado (...) también será puesta en tela de juicio, y se propondrá su reemplazo por una lógica de investigación basada en simulaciones.The purpose of this essay is to show that the standard approach of economic modelling is ineffective for the issue of uncertainty. Since under uncertainty economic agents do not know the final outcome of a course of action, their decisions should not be represented by the process of constrained optimization. The construction of axiomatic systems that lead to the derivation of a single result is also put into question; its replacement by a simulation-based approach is proposed. (shrink)

Doubly robust (DR) estimators are an important class of statistics derived from a theory of semiparametric efficiency. They have become a popular tool in causal inference, including applications to dynamic treatment regimes. The doubly robust estimators for the mean response to a dynamic treatment regime may be conceived through the augmented inverse probability weighted (AIPW) estimating function, defined as the sum of the inverse probability weighted (IPW) estimating function and an augmentation term. The IPW estimating function of the causal estimand (...) via marginal structural model is defined as the complete-case score function for those subjects whose treatment sequence is consistent with the dynamic regime in question divided by the probability of observing the treatment sequence given the subject's treatment and covariate histories. The augmentation term is derived by projecting the IPW estimating function onto the nuisance tangent space and has mean-zero under the truth. The IPW estimator of the causal estimand is consistent if (i) the treatment assignment mechanism is correctly modeled and the AIPW estimator is consistent if either (i) is true or (ii) nested functions of intermediate and final outcomes are correctly modeled.Hence, the AIPW estimator is doubly robust and, moreover, the AIPW is semiparametric efficient if both (i) and (ii) are true simultaneously. Unfortunately, DR estimators can be inferior when either (i) or (ii) is true and the other false. In this case, the misspecified parts of the model can have a detrimental effect on the variance of the DR estimator. We propose an improved DR estimator of causal estimand in dynamic treatment regimes through a technique originally developed by [4] which aims to mitigate the ill-effects of model misspecification through a constrained optimization.In addition to solving a doubly robust system of equations, the improved DR estimator simultaneously minimizes the asymptotic variance of the estimator under a correctly specified treatment assignment mechanism but misspecification of intermediate and final outcome models. We illustrate the desirable operating characteristics of the estimator through Monte Carlo studies and apply the methods to data from a randomized study of integrilin therapy for patients undergoing coronary stent implantation. The methods proposed here are new and may be used to further improve personalized medicine, in general. (shrink)

This article discusses the rationality principle, especially in Popper's version, on the occasion of a commentary of Maurice Lagueux's book, Rationality and Explanation in Economics (2010).

Combinatorial network optimization appears to fit well as a model of brain structure: connections in the brain are a critically constrained resource, hence their deployment in a wide range of cases is finely optimized to “‘save wire". This review focuses on minimization of large-scale costs, such as total volume for mammal dendrite and axon arbors and total wirelength for positioning of connected neural components such as roundworm ganglia (and also mammal cortex areas). Phenomena of good optimization raise (...) questions about mechanisms for their achievement: the examples of optimized neuroanatomy here turn out to include candidates for some of the most complex biological structures known to be derivable purely from simple physical energy minimization processes. Part of the functional role of such fine-tuned wiring optimization may be as a compact strategy for generating self-organizing complex neuroanatomical.. (shrink)

Objective: A deep understanding of the relationship between a scarce drug's dose and clinical response is necessary to appropriately distribute a supply-constrained drug along these lines. Summary of key data: The vast majority of drug development and repurposing during the COVID-19 pandemic – an event that has made clear the ever-present scarcity in healthcare systems –has been ignorant of scarcity and dose optimisation's ability to help address it. Conclusions: Future pandemic clinical trials systems should obtain dose optimisation data, as (...) these appear necessary to enable appropriate scarce resource allocation according to societal values. (shrink)

Despite technological innovations, clinical expertise remains the cornerstone of psychiatry. A clinical expert does not only have general textbook knowledge, but is sensitive to what is demanded for the individual patient in a particular situation. A method that can do justice to the subjective and situation-specific nature of clinical expertise is ethnography. Effective deep brain stimulation (DBS) for obsessive-compulsive disorder (OCD) involves an interpretive, evaluative process of optimizing stimulation parameters, which makes it an interesting case to study clinical expertise. The (...) aim of this study is to explore the role of clinical expertise through an ethnography of the particular case of DBS optimization in OCD. In line with the topic of the special issue this article is a part of, we will also use our findings to reflect on ethnography as a method to study complex phenomena like clinical expertise. This ethnography of DBS optimization is based on 18 months of participant observation and nine in-depth interviews with a team of expert clinicians who have been treating over 80 OCD patients since 2005. By repeatedly observing particular situations for an extended period of time, we found that there are recurrent patterns in the ways clinicians interact with patients. These patterns of clinical practice shape the possibilities clinicians have for making sense of DBS-induced changes in patients’ lived experience and behavior. Collective established patterns of clinical practice are dynamic and change under the influence of individual learning experiences in particular situations, opening up new possibilities and challenges. We conclude that patterns of clinical practice and particular situations are mutually constitutive. Ethnography is ideally suited to bring this relation into view thanks to its broad temporal scope and focus on the life-world. Based on our findings, we argue that clinical expertise not only implies skillful engagement with a concrete situation but also with the patterns of clinical practice that shape what is possible in this specific situation. Given this constraining and enabling role of practices, it is important to investigate them in order to find ways to improve diagnostic and therapeutic possibilities. (shrink)

There is a very apparent conflict between economists and ethicists over the moral standards that should be applied to the managers of business firms. The view of most economists is that moral standards in business are not relevant, beyond the normal personal obligations to speak the truth and observe the law, because profit maximizing behaviour, under market and resource constraints, leads inexorably to social welfare optimization. The opposing view of most humanists is that modern markets are not competitive enough (...) to be constraining, that profit maximization often leads to social harm, and that welfare benefits are unjustly distributed. The article examines the moral constructs in microeconomic theory and the ethical objections to that theory at both the pragmatic and theoretic levels, and concludes that inappropriate assumptions about the nature and worth of human beings in the economic paradigm require the use of moral standards for business decisions and actions. (shrink)

This study examined the effects of mood and risk-taking on divergent and convergent thinking using a Q-learning computation model. The results revealed that while mood was not significantly related to divergent or convergent thinking (as creative thinking types), risk-taking exerted positive effects on divergent thinking in the face of negative rewards. The results were consistent with the representational change theory in insight problem solving. Although this theory accounts directly for insight, the underlying idea of going beyond current contexts and implicit (...) constrains could be applied to creative thinking as well. The results indeed accounted for the relevance of this theory to divergent thinking. The current study is one of the first empirical studies simultaneously examining the role of mood and risk-taking in creativity. In particular, no related studies exist that took a computational approach to estimate the relevant parameters in the framework of dynamic optimization. Our Q learning model enables to distinguish and identify the different roles of mood and risk-taking in updatingQvalues and making decisions. (shrink)

Many criticisms have been made of optimization theory (Laville, 1999a). These objections may be explained by the fact that human rationality is bounded – that decisions are constrained by cognitive limitations (Simon, 1982). In the present paper, I will show that if rationality is bounded, then we must study the processes of decision. My thesis is that cognitive limitations lead to procedural rationality. Although this assertion has already been sustained implicitly by Simon (1959) and explicitly by Mongin (1986), (...) it has not been argued in sufficient depth. (shrink)

An animal confronts numerous challenges when constructing an optimal navigational route. Spatial representations used for path optimization are likely constrained by critical environmental factors that dictate which neural systems control navigation. Multiple coding schemes depend upon their ecological relevance for a particular species, particularly when dealing with the third, or vertical, dimension of space.

This article revisits the standard results of demand theory when the preference relation is a continuous preorder that admits an equicontinuous multi-utility representation. We study the consumer problem as the constrained maximization of a continuous vector-valued utility mapping, and show how to rederive those results. In particular, we provide a link between the literature on vector optimization and the analysis of the consumer problem under incomplete preferences.

Urban space is the spatial projection of various social and economic activities. Given the complexity of urban functions and the ongoing expansion of urban areas, the spatial differentiation of various economic activities within cities tends to become more and more clear; moreover, there tends to be spatial inequalities in resource allocation. Taking Beijing as an example, this study develops a spatial accessibility model at the town level by integrating the spatial distribution of economic activities with the transport system and evaluating (...) the accessibility to various economic activities. The equality of the residents’ economic-related travel activity is also evaluated in line with the population distribution. The results show that the accessibility to economic activities generally decreases in going from the urban center to the peripheral suburbs, and this “core to edge” difference is readily apparent. In general, residents tend to choose areas to settle in which have a high degree of accessibility, however, the attractiveness of accessibility is constrained within certain limits, as evidenced by a reduction of population in the center of Beijing. Additionally, there are inequalities with respect to traveling experiences. For instance, 27.8% of residents experience very convenient travel conditions, mostly in the Xicheng, Dongcheng, and Haidian districts, and this equates to a high level of accessibility; about half of the residents in Beijing live in areas ranked as being of medium accessibility and where the distribution of economic activities and transportation facilities are insufficient. Residents living in the outlying Changping, Mentougou, Shunyi, and Fangshan districts have relatively poor access to transportation indicating a low level of accessibility. Overall, the evaluation method for spatial equality considers comprehensively the distribution of economic activities, transportation, and population distribution and can provide a reference framework for optimization of the urban spatial structure to improve urban spatial equality. (shrink)

The absence of appropriate information about imperceptible and ethical food characteristics limits the opportunities for concerned consumer/citizens to take ethical issues into account during their inescapable food consumption. It also fuels trust crises between producers and consumers, hinders the optimal embedment of innovative technologies, “punishes” in the market ethical producers, and limits the opportunities for politically liberal democratic governance. This paper outlines a framework for the ethical characterization and subsequent optimization of foods (ECHO). The framework applies to “imperceptible,” “pragmatic,” (...) and “reasonable” food characteristics about which consumers/citizens maintain concerns. A political perspective is assumed in that valid information is taken to serve the politically liberal and democratic functions of the market by allowing concerned citizens to make informed choices in their role as food consumers. Information is aggregated by multi-attribute modeling. It takes the form of “maximized” (“utilitarian”) to “most balanced” (MINMAX) non-binary aggregate comparative rankings of perceptibly substitutable food products. The model requires the description of characteristics by means of criteria and weights (structural input), and technical input on the performance of food for these criteria (product input). Structural input is grounded on relevantly concerned citizen/consumers’ perceptions. It is culture and times dependent. Availability of product input is assumed. Uses for the amelioration of the aforementioned limitations are discussed. So long as, and to the extent that, certain ethical concerns are not addressed by public policy, the ECHO framework may facilitate offering members of society a necessary (though not a sufficient) condition for regulating the ethical aspects of food production in self-regulated markets as consumers, when they are constrained to do so through their government as citizens. In doing that, the framework may contribute to the development of the ethical dimension of food production and may bring rewards for food supply actors that take reasonable concerns of citizen/consumers into account. (shrink)

Decisions based on algorithmic, machine learning models can be unfair, reproducing biases in historical data used to train them. While computational techniques are emerging to address aspects of these concerns through communities such as discrimination-aware data mining and fairness, accountability and transparency machine learning, their practical implementation faces real-world challenges. For legal, institutional or commercial reasons, organisations might not hold the data on sensitive attributes such as gender, ethnicity, sexuality or disability needed to diagnose and mitigate emergent indirect discrimination-by-proxy, such (...) as redlining. Such organisations might also lack the knowledge and capacity to identify and manage fairness issues that are emergent properties of complex sociotechnical systems. This paper presents and discusses three potential approaches to deal with such knowledge and information deficits in the context of fairer machine learning. Trusted third parties could selectively store data necessary for performing discrimination discovery and incorporating fairness constraints into model-building in a privacy-preserving manner. Collaborative online platforms would allow diverse organisations to record, share and access contextual and experiential knowledge to promote fairness in machine learning systems. Finally, unsupervised learning and pedagogically interpretable algorithms might allow fairness hypotheses to be built for further selective testing and exploration. Real-world fairness challenges in machine learning are not abstract, constrained optimisation problems, but are institutionally and contextually grounded. Computational fairness tools are useful, but must be researched and developed in and with the messy contexts that will shape their deployment, rather than just for imagined situations. Not doing so risks real, near-term algorithmic harm. (shrink)

This paper examines the explanatory distinctness of wiring optimization models in neuroscience. Wiring optimization models aim to represent the organizational features of neural and brain systems as optimal (or near-optimal) solutions to wiring optimization problems. My claim is that that wiring optimization models provide design explanations. In particular, they support ideal interventions on the decision variables of the relevant design problem and assess the impact of such interventions on the viability of the target system.

The relations between rationality and optimization have been widely discussed in the wake of Herbert Simon's work, with the common conclusion that the rationality concept does not imply the optimization principle. The paper is partly concerned with adding evidence for this view, but its main, more challenging objective is to question the converse implication from optimization to rationality, which is accepted even by bounded rationality theorists. We discuss three topics in succession: (1) rationally defensible cyclical choices, (2) (...) the revealed preference theory of optimization, and (3) the infinite regress of optimization. We conclude that (1) and (2) provide evidence only for the weak thesis that rationality does not imply optimization. But (3) is seen to deliver a significant argument for the strong thesis that optimization does not imply rationality. (shrink)

Merging logic and mathematics in deductive inference-an innovative, cutting-edge approach. Optimization methods for logical inference? Absolutely, say Vijay Chandru and John Hooker, two major contributors to this rapidly expanding field. And even though "solving logical inference problems with optimization methods may seem a bit like eating sauerkraut with chopsticks... it is the mathematical structure of a problem that determines whether an optimization model can help solve it, not the context in which the problem occurs." Presenting powerful, proven (...) optimization techniques for logic inference problems, Chandru and Hooker show how optimization models can be used not only to solve problems in artificial intelligence and mathematical programming, but also have tremendous application in complex systems in general. They survey most of the recent research from the past decade in logic/optimization interfaces, incorporate some of their own results, and emphasize the types of logic most receptive to optimization methods-propositional logic, first order predicate logic, probabilistic and related logics, logics that combine evidence such as Dempster-Shafer theory, rule systems with confidence factors, and constraint logic programming systems. Requiring no background in logic and clearly explaining all topics from the ground up, Optimization Methods for Logical Inference is an invaluable guide for scientists and students in diverse fields, including operations research, computer science, artificial intelligence, decision support systems, and engineering. (shrink)

Mental representations have continuous as well as discrete, combinatorial properties. For example, while predominantly discrete, phonological representations also vary continuously; this is reflected by gradient effects in instrumental studies of speech production. Can an integrated theoretical framework address both aspects of structure? The framework we introduce here, Gradient Symbol Processing, characterizes the emergence of grammatical macrostructure from the Parallel Distributed Processing microstructure (McClelland, Rumelhart, & The PDP Research Group, 1986) of language processing. The mental representations that emerge, Distributed Symbol Systems, (...) have both combinatorial and gradient structure. They are processed through Subsymbolic Optimization–Quantization, in which an optimization process favoring representations that satisfy well-formedness constraints operates in parallel with a distributed quantization process favoring discrete symbolic structures. We apply a particular instantiation of this framework, λ-Diffusion Theory, to phonological production. Simulations of the resulting model suggest that Gradient Symbol Processing offers a way to unify accounts of grammatical competence with both discrete and continuous patterns in language performance. (shrink)

ABSTRACT. The relations between rationality and optimization have been widely discussed in the wake of Herbert Simon’s work, with the common conclusion that the rationality concept does not imply the optimization principle. The paper is partly concerned with adding evidence for this view, but its main, more challenging objective is to question the converse implication from optimization to rationality, which is accepted even by bounded rationality theorists. We discuss three topics in succession: (1) rationally defensible cyclical choices, (...) (2) the revealed preference theory of optimization, and (3) the infinite regress of optimization. We conclude that (1) and (2) provide evidence only for the weak thesis that rationality does not imply optimization. But (3) is seen to deliver a significant argument for the strong thesis that optimization does not imply rationality. (shrink)

Mobile health applications that promise the user to help her with some aspect of her health are very popular: for-profit apps such as MyFitnessPal, Fitbit, or Headspace have tens of millions of users each. For-profit health apps are designed and run as optimization systems. One would expect that these health apps aim to optimize the health of the user, but in reality they aim to optimize user engagement and, in effect, conversion. This is problematic, I argue, because digital health (...) environments that aim to optimize user engagement risk being manipulative. To develop this argument, I first provide a brief analysis of the underlying business models and the resulting designs of the digital environments provided by popular for-profit health apps. In a second step, I present a concept of manipulation that can help analyze digital environments such as health apps. In the last part of the article, I use my concept of manipulation to analyze the manipulative potential of for-profit health apps. Although for-profit health can certainly empower their users, the conditions for empowerment also largely overlap with the conditions for manipulation. As a result, we should be cautious when embracing the empowerment discourse surrounding health apps. An additional aim of this article is to contribute to the rapidly growing literature on digital choice architectures and the ethics of influencing behavior through such choice architectures. I take health apps to be a paradigmatic example of digital choice architectures that give rise to ethical questions, so my analysis of the manipulative potential of health apps can also inform the larger literature on digital choice architectures. (shrink)

This article presents a simple derivation of optimization models for reaction networks leading to a generalized form of the mass-action law, and compares the formal structure of Minimum Information Divergence, Quadratic Programming and Kirchhoff type network models. These optimization models are used in related articles to develop and illustrate the operation of ontology alignment algorithms and to discuss closely connected issues concerning the epistemological and statistical significance of sharp or precise hypotheses in empirical science.

We reopen the investigation into the formal and conceptual relationship between bidirectional optimality theory (Blutner in J Semant 15(2):115–162, 1998 , J Semant 17(3):189–216, 2000 ) and game theory. Unlike a likeminded previous endeavor by Dekker and van Rooij (J Semant 17:217–242, 2000 ), we consider signaling games not strategic games, and seek to ground bidirectional optimization once in a model of rational step-by-step reasoning and once in a model of reinforcement learning. We give sufficient conditions for equivalence of (...) bidirectional optimality and the former, and show based on numerical simulations that bidirectional optimization may be thought of as a process of reinforcement learning with lateral inhibition. (shrink)