Assumption Of Knowledge Research Articles

Os porquês da criança na psicologia genética de Piaget e na psicanálise e a dificuldade de aprendizagem

RESUMO:Aborda-se a dimensão do enigma na dificuldade de aprendizagem e, por conseguinte, a importância dos porquês intermináveis da criança em uma determinada fase. Para isso, trabalha com a interlocução entre o argumento de Piaget e a abordagem psicanalítica acerca dos referidos porquêsinfantis. Argumenta-se que embora a aprendizagem se sustente na suposição do saber do Outro, em muitos quadros, podemos verificar padecimentos nas aprendizagens devido à ausência da sustentação do enigma introduzido pelo saber inconsistente do Outro, cuja matriz são as investigações infantis com todos os seus porquês.

A literatura e suas modalidades de amor: um enfoque psicanalítico

A psicanálise sempre precisou da arte, em especial da literatura, mas também, das artes plásticas e cênicas para revelar o que a teoria não consegue ver, por representar as estruturas e procedimentos do funcionamento psíquico e social. Nestes termos, Lacan vai ler este amor primordial, que é o amor pela própria imagem, sob o signo do mito de Narciso, assim como o amor transferencial, tematizado por Freud, será conjugado sob o signo do Banquete, seja como suposição de saber, que nada sabe além do desejo de Alcebíades por Agatão, seja como discurso do mestre, por alocar o amor philia no lugar do agalmasedutório. A psicanálise, também vai abordar o amor trágico a partir da lição de Antígona, aquela que deu até aquilo que não tinha, em nome da dignidade humana. Já o amor-paixão será articulado sob a rubrica do bovarismo, expressão romântica de uma aspiração amorosa que, em malogro, vai desembocar na melancolia, como antessala da morte. O amor cômico, na pena de Molière, será o amor que rebaixa o Outro, justamente para que o sujeito não se obrigue a morrer de amor. O amor ao pensamento será articulado nos termos daquele que ama o próprio amor, cujo modelo será o amor cortês provençal. Enfim, no derradeiro Lacan, será o amor de Beatriz que iluminará o caminho sublimatório de Dante em direção à significação vazia do amor eterno. Por tudo isto Lacan irá nos lembrar, em sua homenagem a Marguerite Duras, que o artista sempre irá preceder o psicanalista nos trilhamentos do inaudito, tanto no que se refere ao saber, quanto ao amor...

Making the most of data: An information selection and assessment framework to improve water systems operations

AbstractAdvances in Environmental monitoring systems are making a wide range of data available at increasingly higher temporal and spatial resolution. This creates an opportunity to enhance real‐time understanding of water systems conditions and to improve prediction of their future evolution, ultimately increasing our ability to make better decisions. Yet, many water systems are still operated using very simple information systems, typically based on simple statistical analysis and the operator's experience. In this work, we propose a framework to automatically select the most valuable information to inform water systems operations supported by quantitative metrics to operationally and economically assess the value of this information. The Hoa Binh reservoir in Vietnam is used to demonstrate the proposed framework in a multiobjective context, accounting for hydropower production and flood control. First, we quantify the expected value of perfect information, meaning the potential space for improvement under the assumption of exact knowledge of the future system conditions. Second, we automatically select the most valuable information that could be actually used to improve the Hoa Binh operations. Finally, we assess the economic value of sample information on the basis of the resulting policy performance. Results show that our framework successfully select information to enhance the performance of the operating policies with respect to both the competing objectives, attaining a 40% improvement close to the target trade‐off selected as potentially good compromise between hydropower production and flood control.

An adaptive scheme for chaotic synchronization in the presence of uncertain parameter and disturbances

Recently, several schemes have been proposed in the literature to synchronize chaotic systems. However, in most of these approaches, the presence of uncertain parameters and external disturbances were not considered. Motivated by the above consideration, this paper proposes an adaptive methodology to synchronize any chaotic system with unified chaotic systems, even if bounded disturbances are present. The proposed controller is composed of both variable proportional and adaptive control actions for guaranteeing the convergence of the residual synchronization error to zero in the presence of disturbances. Two possible modifications are considered: 1) only adaptive control action is implemented to overcome the well-known assumption of prior knowledge of upper bounds to compensate for the disturbances, and 2) the control gain of the proportional part is saturated, when the residual synchronization error has, practically, been removed. Lyapunov theory, in combination with Barbalat׳s Lemma, is used to design the proposed controller. Experimental simulations are provided to show the effectiveness of the proposed controller and its advantages, when compared with a recent work in the literature.

The devil is in the detail: unstable response functions in species distribution models challenge bulk ensemble modelling

AbstractAimSpecies distribution models (SDMs) are commonly used to determine threats to biodiversity and opportunities under climate change. Despite SDMs being based on the assumption of complete knowledge of the climate space of the modelled species, truncated occurrence datasets (and hence truncated climate spaces) such as national inventories are often employed. This may lead to prediction errors, which have been proposed to stem from: (1) the degree of climate space truncation and/or (2) instability of the modelling algorithms. Our aim was to explore the potential causes of prediction errors in SDMs using truncated training datasets.LocationEurope 11° W–32° E, 34°–72° N.MethodsSDMs employing commonly used bioclimatic variables were applied to seven forest tree species. We created two model training datasets covering: (1) Germany only (significantly truncated climate space) and (2) Europe (minimally truncated climate space). Differences between the climate space represented by Germany‐only and European data were measured on two‐dimensional climate spaces obtained through principal component analysis of the bioclimatic variables. Seven SDM algorithms were run, and the stability of the response function and variable selection for each species and model type were analysed.ResultsThe degree of climate space truncation was less important for model performance than the instability of model algorithms and indiscriminate variable selection. The latter led to irrelevant relationships of species occurrence with bioclimatic variables. These instabilities caused pronounced prediction errors.Main conclusionsOur results strongly suggest that erroneous model predictions stem from instability and ecological irrelevance of the statistical functions relating the probability of a species' occurrence to bioclimatic variables, compounded by a lack of consistency in variable selection. Models displaying these characteristics showed lower overall performance when trained with truncated datasets. Further, commonly used ensemble approaches do not compensate for the shortfalls of individual models. Detailed model‐by‐model and species‐by‐species analysis of response functions and variable importance is recommended.

A remark on multiobjective stochastic optimization via strongly convex functions

Many economic and financial applications lead (from the mathematical point of view) to deterministic optimization problems depending on a probability measure. These problems can be static (one stage), dynamic with finite (multistage) or infinite horizon, single objective or multiobjective. We focus on one-stage case in multiobjective setting. Evidently, well known results from the deterministic optimization theory can be employed in the case when the “underlying” probability measure is completely known. The assumption of a complete knowledge of the probability measure is fulfilled very seldom. Consequently, we have mostly to analyze the mathematical models on the data base to obtain a stochastic estimate of the corresponding “theoretical” characteristics. However, the investigation of these estimates has been done mostly in one-objective case. In this paper we focus on the investigation of the relationship between “characteristics” obtained on the base of complete knowledge of the probability measure and estimates obtained on the (above mentioned) data base, mostly in the multiobjective case. Consequently we obtain also the relationship between analysis (based on the data) of the economic process characteristics and “real” economic process. To this end the results of the deterministic multiobjective optimization theory and the results obtained for stochastic one objective problems will be employed.

The general theory of decisions

The majority of the existing decision theories, starting from von Neumann and Morgenstern Expected Utility theory, are based on sound mathematical background and yielded good results. However, they are developed for solving particular decision situations. Consequently, nowadays there is no theory to unite main directions of decision analysis. The existing main theories suffer from several important disadvantages: (i) use of numerical techniques which contrast with real-world imperfect information; (ii) the assumptions of well-structured knowledge about future objective conditions; (iii) the use of probability measures whereas real-world probabilities are imprecise; (iv) the use binary logic-based preference relations, whereas real preferences may be vague; (v) no account for the fact that a human being reasons with linguistic description of information; (vi) parametrical modeling of behavioral determinants without account for interaction; (vii) missing partial reliability of real decision-relevant information. Thus, it becomes needed to develop a general theory of decisions that would be free of the limitations outlined above. In this paper, we propose the fundamentals of the new general theory of decisions which is based on complex consideration of imperfect decision-relevant information issues and behavioral aspects. We illustrate that the existing theories including Expected Utility of von Neumann and Morgenstern, Prospect Theory, Choquet Expected Utility, Cumulative Prospect Theory and other theories are special cases of the suggested general theory of decisions. We provide axioms and principles, the corresponding mathematical methodologies of decision analysis and auxiliary formal techniques. The application of the suggested theory is illustrated with the aid of a benchmark complex decision problem.

FAIR PRICE AND TRADING PRICE: AN ABM APPROACH WITH ORDER-PLACEMENT STRATEGY AND MISUNDERSTANDING OF FUNDAMENTAL VALUE

This paper studies the effect of investor’s bounded rationality on market dynamics. In a call auction market, we consider a few-types model where two risky assets are traded. Agents differ by their behavior, knowledge, risk aversion and investment horizon. The investor’s demand is defined by a utility maximization under constant absolute risk aversion (CARA). Relaxing the assumption of perfect knowledge of fundamentals enables to identify two components in a bubble. The first one comes from the unperceived fundamental changes due to trader’s belief perseverance. The second one is generated by chartist behavior. In all simulations, speculators make the market less efficient and more volatile. They also increase the maximum amount of assets exchanged in the most liquid time step. However, our model does not show rising average volatility on long term. Concerning the fundamentalists, the belief perseverance has a stabilization impact on the spot price. The closer the anchor is to the true fundamental value, the more efficient the market is, because the prices change smoothly.

Theoretical analysis of errors when estimating snow distribution through point measurements

Abstract. In recent years, marked improvements in our knowledge of the statistical properties of the spatial distribution of snow properties have been achieved thanks to improvements in measuring technologies (e.g., LIDAR, terrestrial laser scanning (TLS), and ground-penetrating radar (GPR)). Despite this, objective and quantitative frameworks for the evaluation of errors in snow measurements have been lacking. Here, we present a theoretical framework for quantitative evaluations of the uncertainty in average snow depth derived from point measurements over a profile section or an area. The error is defined as the expected value of the squared difference between the real mean of the profile/field and the sample mean from a limited number of measurements. The model is tested for one- and two-dimensional survey designs that range from a single measurement to an increasing number of regularly spaced measurements. Using high-resolution (~ 1 m) LIDAR snow depths at two locations in Colorado, we show that the sample errors follow the theoretical behavior. Furthermore, we show how the determination of the spatial location of the measurements can be reduced to an optimization problem for the case of the predefined number of measurements, or to the designation of an acceptable uncertainty level to determine the total number of regularly spaced measurements required to achieve such an error. On this basis, a series of figures are presented as an aid for snow survey design under the conditions described, and under the assumption of prior knowledge of the spatial covariance/correlation properties. With this methodology, better objective survey designs can be accomplished that are tailored to the specific applications for which the measurements are going to be used. The theoretical framework can be extended to other spatially distributed snow variables (e.g., SWE – snow water equivalent) whose statistical properties are comparable to those of snow depth.

The Economic Man

This paper questions how economic systems are using knowledge to support economic decision making. Scientific knowledge collected by centralized planners is not the right type of information to base economic decisions on since it does not have the capacity to consider local opportunity factors. These concerns, originally espoused by Frederick Hayek when analyzing the economy as a whole, can be decomposed to focus on individual businesses. A large corporation, encumbered with a traditional vertical organizational structure, typically has an executive management team taking on the role of the centralized planners in Hayek’s model, and is forced to make decisions without the local opportunity knowledge that may be available in their low-level business units. Economic decisions must fundamentally be based on accurate information that considers local opportunities, and must be made rapidly in today’s dynamic market place. To get access to local knowledge from their low-level business units, executive management teams should consider implementing learning organizations with horizontal management structures that encourage collaboration. Companies with centralized organizational structures simply move too slowly to be competitive. The “Economic Man” is an economist’s creation designed by Mr. Hayek to show how unlikely it is that economic decision makers could ever accurately predict future economic conditions. A hypothetical “Economic Man” with “perfect foresight” was described, in essence, to allow readers to visualize the futility of the assumptions underlying our current economic theories. Yet in today’s competitive business environment, perfect foresight is precisely the standard we impose on our executive managers. The perfect knowledge assumption is said to mislead these economic decision makers into a false believe the problem is simpler, from the perspective of the information needed to support such decisions, than it actually is. This results in poor investment decisions by managers that collect the wrong economic data. The division of labor concept notices that complex jobs can be more efficiently completed by a number of workers performing specialized tasks, than by one worker trying to complete the entire job. By dividing up the required skills on complex tasks across multiple workers, they can become specialists and work their assigned area more efficiently. The level of knowledge required to make workers skilled specialists is essential for the division of labor concept to be effective. Further knowledge is required to quantify economic input variables to the extent that decision makers in executive management can accurately predict how the marketplace will unfold and, in so doing, achieve an "economic man" like stature. No matter how we materialize or describe the problem; it seems the effective acquisition and use of knowledge is the correct answer. Peter Drucker relates the economic successes a corporation has with their ability to create “core competencies” that lead to market leadership positions. He begins to abstractly define the type of data that should be collected to help managers of these corporations monitor the strength and relative position of their core competencies. This relates to Mr. Hayek’s concern that managers need accurate, but relevant, information to base their decisions on. Just when things were looking bleak for our hypothetical “Economic Man,” these important steps define feasible types of real time information processing that can support economic decision makers monitoring the strength and position of core competencies. While it’s not quite as good as the perfect foresight imposed on our “Economic Man,” relevant, near real time economic data is a giant leap forward.

Modal analysis of structures under non-stationary excitation

The search for mathematical–numerical models to be applied in the design or optimization of civil structures may require adjustment and validation based on experimental data. However, in many cases, when performing dynamic assessment in field structures, it is common to face the situation where it is impossible to stop the running of certain equipment. In this case, all measurements performed on the building and the neighboring structures will be corrupted by noise generated during the operation of these machines. Clearly, many parameter estimation algorithms used to characterize the dynamic behavior of a structure, which are based on the assumption of prior knowledge of the input force in their formulations, may give poor results. In this context, a modal analysis procedure based on the adaptive filter RLS/QRD is described. In this analysis, the measured vibration signal is corrupted by a harmonic noise, in particular, in the presence of signals from the actual machine or other nearby machines. The specific characteristics of the modeling and the hypotheses associated with the harmonic signals are explained. The fact that the excitation force is considered not to be time-invariant in the model, but the frequency varies over time, allows the modal parameters to be identified even under non-stationary conditions. In order to investigate the influence of harmonic noise on the estimated values, a numerical model of the ASCE benchmark structure was used to simulate some practical situations.

Uncertainty in reservoir modeling

Uncertainty is due to incomplete and imprecise knowledge as a result of limited sampling of the subsurface heterogeneities. Well data and seismic data have incomplete coverage and finite resolution. The interpretations are uncertain. Reservoirs are heterogeneous and difficult to predict away from wells. Ignoring uncertainty and locking in important model parameters and choices amounts to an assumption of perfect knowledge and is generally an unacceptable approach. Uncertainty must be explicitly modeled. Understanding the (1) sources of uncertainty, (2) methods to represent uncertainty, (3) the formalisms of uncertainty, and (4) uncertainty modeling methods and workflows were essential for the integration of all reservoir information sources and providing good models for decision making in the presence of uncertainty.

Application of Hazard Based Model for Choice Set Formation of Housing Location

The problem of choice set formation for decision makers is an important subject in discrete choice modeling especially when the choice set contains a large number of elemental alternatives. In general, the choice set of an individual could be a randomly sampled choice set; however, this is claimed to be a behaviorally inacceptable practice due to the fallacious assumption of full knowledge of individuals about potential random choices. This brings up the need to come up with methods to logically allocate credible choice alternatives for individuals. This article attempts to provide a detailed study of this screening and filtering practice to develop a modeling framework that can replicate the choice set formation process. In order to show the potential of the method, two decision criteria — affordability and maximum distance to work — are considered the potential attribute that the household evaluates for feasible housing alternatives. It is postulated that alternatives will only be included in the choice set if the maximum work distance and price satisfy the household thresholds. This article explores the application of proportional hazard models in the housing search process. Some of the specifications of hazard-based models that are typically used on temporal data are examined on work distance and price. The study has used the household travel behavior survey conducted by Chicago Metropolitan Agency for Planning (CMAP). Furthermore, several extensive land use and transportation related data sources are incorporated to complement the scope of the modeling results.

Erratum to “Optimal Reverse-Pricing Mechanisms” by Martin Spann, Robert Zeithammer, and Gerald Häubl

In our paper about optimal reverse pricing mechanisms [Spann M, Zeithammer R, Häubl G (2010) Optimal reverse-pricing mechanisms. Marketing Sci. 29(6):1058–1070] (hereafter, ORPM), some of the mathematical derivations implicitly assume that the name-your-own-price seller interprets the outside-market posted price p differently than the buyers. This note shows that all of the qualitative results in ORPM continue to hold under the more natural assumption of common knowledge that p is the upper bound of wholesale cost. Interestingly, the proofs and algebraic expressions are often simpler than those in ORPM.

Asymmetries and Markov-switching structural VAR

The development of nonlinear representations and of generalized IRFs favored the study of the variables behavior in response to an economically identified shock as regards (i) the state of the system when the shock occurs, (ii) the size of the shock and (iii) the sign of the shock. Generalized IRFs are widely used in threshold representations to illustrate and even test the presence of asymmetries (Potter, 1995; van Dijk et al., 2007). However, GIRF have known no comparable development in Markov-switching VAR. I show that whether IRF and GIRF recently developed in this framework impose sign and size symmetries or display poor properties. In this paper, I propose a new GIRF for general Markov-switching structural VAR with fixed transition probabilities. In a simulation framework inspired from Rubio-Ramirez et al. (2005), I relax the assumption of perfect knowledge of the regime and introduce the updating step proposed by Camacho and Perez-Quiros (2013). As a consequence, sign/size asymmetries can now be examined in MSIAH-VAR and the GIRF now incorporates the dependence on future shocks without extra complexity. Visited regimes can now differ endogenously between the shocked and baseline trajectories due to the initial and future shocks but the only exogenous difference in the simulated trajectories relies on the initial structural shock. I use this approach to implement a test for sign and size asymmetries on US aggregate gross job flows.

The Sum-Capacity of the Ergodic Fading Gaussian Cognitive Interference Channel

This paper characterizes the sum-capacity of the ergodic fading Gaussian overlay cognitive interference channel (EGCIFC), a time-varying channel with two source-destination pairs in which a primary/licensed transmitter and a secondary/cognitive transmitter share the same spectrum and where the cognitive transmitter has noncausal knowledge of the primary user's message. The throughput/sum-capacity is characterized under the assumption of perfect knowledge of the instantaneous fading states at all terminals, which are assumed to form an ergodic process. A genie-aided outer bound on the sum-capacity is developed and then matched with an achievable scheme, thereby completely characterizing the sum-capacity of the EGCIFC. The power allocation policy that maximizes the sum-capacity is derived. It is shown that the sum-capacity achieving scheme for an EGCIFC is “separable” in all regimes (i.e., coding across fading states is not necessary), as opposed to the classical interference channel. Extensions to the whole capacity region are discussed. As a capacity achieving scheme for the EGCIFC under certain channel gain conditions, and as a topic of independent interest, the ergodic capacity of a point-to-point multiple-input-single-output channel with per-antenna power constraints and with perfect channel state information at all terminals is also derived.

The emergence of a knowledge-based theory of internationalisation

This paper charts the development of the knowledge-based theory of internationalisation, driven particularly by early research at the University of Uppsala, led by Sune Carlson. Information and knowledge, allied with an interest in the associated effect on risk and uncertainty, were components of a process perspective. Its essence was a focus on the restraining effects of a lack of knowledge as decision-makers contemplated international market entry or expansion requiring a commitment of resources in various forms, in different types of foreign operations (such as exporting, licensing and foreign direct investment). Lack of knowledge of a prospective foreign market (of its characteristics, culture, ways of doing business) was seen to create uncertainty so that firm decision-makers would be less prepared to commit resources. This situation was bound to change as a firm conducted operations in the foreign market and acquired experiential knowledge (learning by doing), which made the foreign market less of a mystery, in the process lowering uncertainty. As the learning process unfolded, and expanded opportunities were perceived, at some stage the firm might be prepared to undertake additional commitments to the foreign market. Empirical research at Uppsala, and in Finland, had shown a pattern of gradual expansion of foreign commitments by internationalising firms. In terms of theoretical development, a key step was relaxation of the assumption of perfect knowledge used in economics (not without critique), noted by Don Lamberton in 1974. Following the development of internationalisation theory in the 1970s, there was a range of extensions to the basic theory, such as the role of networks, the nature of inward–outward connections, and the need for knowledge to pass over language hurdles in the process of international transmission – within and outside the firm. In one sense, the internationalisation of companies was a perfect research site for an exploration of the role of information and knowledge in firm behaviour, given the additional exigencies of the diverse and demanding information environment that is the international arena.

Commentary on the Discussion Paper of Marilyn Fischer, “Addams on Cultural Pluralism, European Immigrants, and African Americans”

with her usual concern with accuracy and clarity, Marilyn Fischer’s explanations are exemplary models of the value of historical scholarship. Concern with context in its many forms is integral to pragmatist philosophy, but the range and depth of Fischer’s research make her papers especially valuable. She helps us understand the extent to which the horizon of understanding is bounded by the particularities of time and place. Careful elucidation of less familiar concrete horizons can give us a better understanding of unfamiliar beliefs and values and illuminate our own pre-suppositions. Such concreteness cuts two ways. It can re-invigorate current debates by challenging our assumptions of what particular texts mean based on what we assume they meant at a particular time or place. Such contextual contingencies are at the same time boundaries that contain and constrain. They focus interpretation on the state of knowledge and cultural assumptions that have since been discarded or bypassed or that have been newly acquired rather than keep in play shifts in perspective from past to present or from the familiar to the unfamiliar. In both cases, re-evaluation is required, and its viability and worth for us depend on the hermeneutical or interpretive rules used (see Seigfried, “Role of Place”). Fischer’s paper is especially good at clarifying the confounding of ethnicity and race in the culture at large during the first decades of Hull House. Ethnicity was not only racialized, but also hierarchized according to Lamarkian assumptions of progress. The supposedly “superior” Anglo-Saxon “race” was at the top, and the so-called “inferior” Southern European and Eastern European peasants and African Americans were at the bottom. Given that Addams’s experience and theorizing focused on European immigrants, their linkage with African Americans in the public and, indeed, the academic mind,

Primary User Traffic Classification in Dynamic Spectrum Access Networks

This paper focuses on analytical studies of the primary user (PU) traffic classification problem. Observing that the gamma distribution can represent positively skewed data and exponential distribution (popular in communication networks performance analysis literature) it is considered here as the PU traffic descriptor. We investigate two PU traffic classifiers utilizing perfectly measured PU activity (busy) and inactivity (idle) periods: (i) maximum likelihood classifier (MLC) and (ii) multi-hypothesis sequential probability ratio test classifier (MSPRTC). Then, relaxing the assumption on perfect period measurement, we consider a PU traffic observation through channel sampling. For a special case of negligible probability of PU state change in between two samplings, we propose a minimum variance PU busy/idle period length estimator. Later, relaxing the assumption of the complete knowledge of the parameters of the PU period length distribution, we propose two PU traffic classification schemes: (i) estimate-then-classify (ETC), and (ii) average likelihood function (ALF) classifiers considering time domain fluctuation of the PU traffic parameters. Numerical results show that both MLC and MSPRTC are sensitive to the periods measurement errors when the distance among distribution hypotheses is small, and to the distribution parameter estimation errors when the distance among hypotheses is large. For PU traffic parameters with a partial prior knowledge of the distribution, the ETC outperforms ALF when the distance among hypotheses is small, while the opposite holds when the distance is large.

Knowledge Assumptions amongst Doctoral Students: A Case Study at the University of South Africa

This study explores the correlation between a group of South African doctoral students’ assumptions about knowledge or epistemological beliefs and their behaviour applicable to research information gathering activities while compiling their research proposals for the doctorate. Six divergent models of epistemological development from the educational psychology (in chronological order) as well as an information search process model formed the conceptual, theoretical framework for the investigation. Ten enrolled doctoral students from the College of Education at the University of South Africa were interviewed about this correlation. The findings provide a rich theoretical foundation for future information-seeking behaviour research among doctoral students and provide insights into the impact of students’ epistemological beliefs on their information seeking patterns. The findings furthermore confirm the correlation between core epistemological beliefs and information gathering patterns amongst South African doctoral students and provide substantial justification for using epistemological beliefs in the supervision of doctoral students. DOI: 10.5901/mjss.2014.v5n20p1068