Reasoning with linguistic preferences using NPN logic

Negative-positive-neutral logic provides an alternative framework for fuzzy cognitive maps development and decision analysis. This paper reviews basic notion of NPN logic and NPN relations and proposes adaptive approach to causality weights assessment. It employs linguistic models of causality weights activated by measurement-based fuzzy cognitive maps' concepts values. These models allow for quasi-dynamical adaptation to the change of concepts values, providing deeper understanding of possible side effects. Since in the real-world environments almost every decision has its consequences, presenting very valuable portion of information upon which we also make our decisions, the knowledge about the side effects enables more reliable decision analysis and directs actions of decision maker

WEATHER LORE VALIDATION TOOL USING FUZZY COGNITIVE MAPS BASED ON COMPUTER VISION

Published ThesisThe creation of scientific weather forecasts is troubled by many technological challenges (Stern & Easterling, 1999) while their utilization is generally dismal. Consequently, the majority of small-scale farmers in Africa continue to consult some forms of weather lore to reach various cropping decisions (Baliscan, 2001). Weather lore is a body of informal folklore (Enock, 2013), associated with the prediction of the weather, and based on indigenous knowledge and human observation of the environment. As such, it tends to be more holistic, and more localized to the farmers’ context. However, weather lore has limitations; for instance, it has an inability to offer forecasts beyond a season. Different types of weather lore exist, utilizing almost all available human senses (feel, smell, sight and hearing). Out of all the types of weather lore in existence, it is the visual or observed weather lore that is mostly used by indigenous societies, to come up with weather predictions. On the other hand, meteorologists continue to treat this knowledge as superstition, partly because there is no means to scientifically evaluate and validate it. The visualization and characterization of visual sky objects (such as moon, clouds, stars, and rainbows) in forecasting weather are significant subjects of research. To realize the integration of visual weather lore in modern weather forecasting systems, there is a need to represent and scientifically substantiate this form of knowledge. This research was aimed at developing a method for verifying visual weather lore that is used by traditional communities to predict weather conditions. To realize this verification, fuzzy cognitive mapping was used to model and represent causal relationships between selected visual weather lore concepts and weather conditions. The traditional knowledge used to produce these maps was attained through case studies of two communities (in Kenya and South Africa).These case studies were aimed at understanding the weather lore domain as well as the causal effects between metrological and visual weather lore. In this study, common astronomical weather lore factors related to cloud physics were identified as: bright stars, dispersed clouds, dry weather, dull stars, feathery clouds, gathering clouds, grey clouds, high clouds, layered clouds, low clouds, stars, medium clouds, and rounded clouds. Relationships between the concepts were also identified and formally represented using fuzzy cognitive maps. On implementing the verification tool, machine vision was used to recognize sky objects captured using a sky camera, while pattern recognition was employed in benchmarking and scoring the objects. A wireless weather station was used to capture real-time weather parameters. The visualization tool was then designed and realized in a form of software artefact, which integrated both computer vision and fuzzy cognitive mapping for experimenting visual weather lore, and verification using various statistical forecast skills and metrics. The tool consists of four main sub-components: (1) Machine vision that recognizes sky objects using support vector machine classifiers using shape-based feature descriptors; (2) Pattern recognition–to benchmark and score objects using pixel orientations, Euclidean distance, canny and grey-level concurrence matrix; (3) Fuzzy cognitive mapping that was used to represent knowledge (i.e. active hebbian learning algorithm was used to learn until convergence); and (4) A statistical computing component was used for verifications and forecast skills including brier score and contingency tables for deterministic forecasts. Rigorous evaluation of the verification tool was carried out using independent (not used in the training and testing phases) real-time images from Bloemfontein, South Africa, and Voi-Kenya. The real-time images were captured using a sky camera with GPS location services. The results of the implementation were tested for the selected weather conditions (for example, rain, heat, cold, and dry conditions), and found to be acceptable (the verified prediction accuracies were over 80%). The recommendation in this study is to apply the implemented method for processing tasks, towards verifying all other types of visual weather lore. In addition, the use of the method developed also requires the implementation of modules for processing and verifying other types of weather lore, such as sounds, and symbols of nature. Since time immemorial, from Australia to Asia, Africa to Latin America, local communities have continued to rely on weather lore observations to predict seasonal weather as well as its effects on their livelihoods (Alcock, 2014). This is mainly based on many years of personal experiences in observing weather conditions. However, when it comes to predictions for longer lead-times (i.e. over a season), weather lore is uncertain (Hornidge & Antweiler, 2012). This uncertainty has partly contributed to the current status where meteorologists and other scientists continue to treat weather lore as superstition (United-Nations, 2004), and not capable of predicting weather. One of the problems in testing the confidence in weather lore in predicting weather is due to wide varieties of weather lore that are found in the details of indigenous sayings, which are tightly coupled to locality and pattern variations(Oviedo et al., 2008). This traditional knowledge is entrenched within the day-to-day socio-economic activities of the communities using it and is not globally available for comparison and validation (Huntington, Callaghan, Fox, & Krupnik, 2004). Further, this knowledge is based on local experience that lacks benchmarking techniques; so that harmonizing and integrating it within the science-based weather forecasting systems is a daunting task (Hornidge & Antweiler, 2012). It is partly for this reason that the question of validation of weather lore has not yet been substantially investigated. Sufficient expanded processes of gathering weather observations, combined with comparison and validation, can produce some useful information. Since forecasting weather accurately is a challenge even with the latest supercomputers (BBC News Magazine, 2013), validated weather lore can be useful if it is incorporated into modern weather prediction systems. Validation of traditional knowledge is a necessary step in the management of building integrated knowledge-based systems. Traditional knowledge incorporated into knowledge-based systems has to be verified for enhancing systems’ reliability. Weather lore knowledge exists in different forms as identified by traditional communities; hence it needs to be tied together for comparison and validation. The development of a weather lore validation tool that can integrate a framework for acquiring weather data and methods of representing the weather lore in verifiable forms can be a significant step in the validation of weather lore against actual weather records using conventional weather-observing instruments. The success of validating weather lore could stimulate the opportunity for integrating acceptable weather lore with modern systems of weather prediction to improve actionable information for decision making that relies on seasonal weather prediction. In this study a hybrid method is developed that includes computer vision and fuzzy cognitive mapping techniques for verifying visual weather lore. The verification tool was designed with forecasting based on mimicking visual perception, and fuzzy thinking based on the cognitive knowledge of humans. The method provides meaning to humanly perceivable sky objects so that computers can understand, interpret, and approximate visual weather outcomes. Questionnaires were administered in two case study locations (KwaZulu-Natal province in South Africa, and Taita-Taveta County in Kenya), between the months of March and July 2015. The two case studies were conducted by interviewing respondents on how visual astronomical and meteorological weather concepts cause weather outcomes. The two case studies were used to identify causal effects of visual astronomical and meteorological objects to weather conditions. This was followed by finding variations and comparisons, between the visual weather lore knowledge in the two case studies. The results from the two case studies were aggregated in terms of seasonal knowledge. The causal links between visual weather concepts were investigated using these two case studies; results were compared and aggregated to build up common knowledge. The joint averages of the majority of responses from the case studies were determined for each set of interacting concepts. The modelling of the weather lore verification tool consists of input, processing components and output. The input data to the system are sky image scenes and actual weather observations from wireless weather sensors. The image recognition component performs three sub-tasks, including: detection of objects (concepts) from image scenes, extraction of detected objects, and approximation of the presence of the concepts by comparing extracted objects to ideal objects. The prediction process involves the use of approximated concepts generated in the recognition component to simulate scenarios using the knowledge represented in the fuzzy cognitive maps. The verification component evaluates the variation between the predictions and actual weather observations to determine prediction errors and accuracy. To evaluate the tool, daily system simulations were run to predict and record probabilities of weather outcomes (i.e. rain, heat index/hotness, dry, cold index). Weather observations were captured periodically using a wireless weather station. This process was repeated several times until there was sufficient data to use for the verification process. To match the range of the predicted weather outcomes, the actual weather observations (measurement) were transformed and normalized to a range [0, 1].In the verification process, comparisons were made between the actual observations and weather outcome prediction values by computing residuals (error values) from the observations. The error values and the squared error were used to compute the Mean Squared Error (MSE), and the Root Mean Squared Error (RMSE), for each predicted weather outcome. Finally, the validity of the visual weather lore verification model was assessed using data from a different geographical location. Actual data in the form of daily sky scenes and weather parameters were acquired from Voi, Kenya, from December 2015 to January 2016.The results on the use of hybrid techniques for verification of weather lore is expected to provide an incentive in integrating indigenous knowledge on weather with modern numerical weather prediction systems for accurate and downscaled weather forecasts

Startup’s critical failure factors dynamic modeling using FCM

The emergence of startups and their influence on a country's economic growth has become a significant concern for governments. The failure of these ventures leads to substantial depletion of financial resources and workforce, resulting in detrimental effects on a country's economic climate. At various stages of a startup's lifecycle, numerous factors can affect the growth of a startup and lead to failure. Numerous scholars and authors have primarily directed their attention toward studying the successes of these ventures. Previous research review of critical failure factors (CFFs) reveals a dearth of research that comprehensively investigates the introduction of all failure factors and their interdependent influences. This study investigates and categorizes the failure factors across various stages of a startup's life cycle to provide a deeper insight into how they might interact and reinforce one another. Employing expert perspectives, the authors construct fuzzy cognitive maps (FCMs) to visualize the CFFs within entrepreneurial ventures and examine these factors' influence across the four growth stages of a venture. Our primary aim is to construct a model that captures the complexities and uncertainties surrounding startup failure, unveiling the concealed interconnections among CFFs. The FCMs model empowers entrepreneurs to anticipate potential failures under diverse scenarios based on the dynamic behavior of these factors. The proposed model equips entrepreneurs and decision-makers with a comprehensive understanding of the collective influence exerted by various factors on the failure of entrepreneurial ventures

Cognitive Maps

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Probabilistic Logics in Foresight

A prudent decision-maker facing a complicated strategic decision considers the factors relevant to the decision, gathers information about the identified factors, and attempts to formulate the best course of action based on the available information. Careful consideration of any alternative course of action might reveal that in addition to the desirable intended consequences, a number of less desirable outcomes are likely to follow as well. Facing a complicatedly entangled net of considerations, entwined positive and negative outcomes, and uncertainty, the decision-maker will attempt to organize the available information and make the decision by using some strategy of reasoning on the information. A logic is away of reasoning adherent to rules, based on structured knowledge. A modeling language and inference rules comprise a logic. The language of a logic is formal, consisting of a defined set of building blocks having well defined meanings. The decision-maker can use a modeling language to describe the information pertinent to the decision-making problem, and organize the information by giving it a structure, which specifies the relationships between the individual considerations. While reasoning about the extensive amount of information in its disorganized form may be overwhelming, in a structured form the information becomes much more useful for the decision-maker, as nowit can be analyzed in a systematic fashion. Inference is systematic reasoning about structured information. As the information is described in a formal and structured way and the process of reasoning about it is systematic, the inference may be automated. Computational inference permits reasoning that would not be possible by intuition in cases where the amount of considerations and their interdependencies exceeds human cognitive capacity. The decision-maker may direct the efforts to describing the decision factors and knowledge with the formal language, with a narrower and more manageable frame of attention, and perform the inference with a computer. Probabilistic language gives room for haziness in knowledge description, and is thus suitable for describing knowledge originating from humans, conveyed to the decision-maker in a non-formal format, such as viewpoints and opinions. Many domains of decision-making and planning use human sourced knowledge, especially if the informants are knowledgeable people or experts with relevant, developed understanding on the domain issues. The expert views can augment the knowledge bases in cases where other forms of information, such as empirical or statistical data, are lacking or completely absent, or do not capture or represent considerations important for the decision-making. This is a typical setting for strategic decision-making, long range planning, and foresight, which have to account for developments and phenomena that do not yet exist in the form they might in the future, or at all. This work discusses approaches for decision support and foresight oriented modeling of expert knowledge bases and inference based on such knowledge bases. Two novel approaches developed by the author are presented and positioned against previous work on cross-impact analysis, structural and morphological analysis, and Bayesian networks. The proposed approaches are called EXIT and AXIOM. EXIT is a conceptually simple approach for structural analysis, based on a previously unutilized computational process for discovery of higher-order influences in a structural model. The analytical output is, in relation to comparable approaches, easier to interpret considering the causal information content of the structural model. AXIOM is a versatile probabilistic logic, combining ideas of structural analysis, morphological analysis, cross-impact analysis and Bayesian belief networks. It provides outputs comparable to Bayesian networks, but has higher fitness for full model parameterization through expert elicitation. A guiding idea of the methodological development work has been that the slightly aged toolset of cross-impact analysis can be updated, improved and extended, and brought to be more interoperable with the Bayesian approach

Essays in household finance in China

This PhD thesis presents three novel essays on household finance in China, using the Chinese Household Finance Survey (CHFS) data of 2013 and 2015. Its identification strategies involve quasi-experimental methods to identify the effects of social, education, and economic reforms on household financial outcomes later in life, along with an inquiry on the impact of financial inclusion and formal financial market participation on the well-being of the Chinese population. All three essays are novelties in the related literature and the use of the CHFS. They offer insights to the academic literature and policy making, regarding the importance of future reforms to household finances and well-being. Reforms that emphasize on the development of skills related to financial knowledge, reforms that aim to strengthen the financial resilience of the Chinese population, and reforms that are conducive to formal financial market participation and behavioural change are likely to be conducive to sustainable development in the Chinese economy, inequality reduction, and welfare enhancement among the Chinese. The first essay examines the effect of education on financial market participation and portfolio choice in China. The identification strategy uses the exogenous variation in years of compulsory schooling that arose from a major reform in the late 1980s, combined with the overlapping single-child policy of 1980, which applied financial constraints on school attendance for noncompliant households. Using a fuzzy regressions discontinuity design that instruments the years of schooling with reform exposure, I find that schooling has a large influence on participation in markets for stocks and risky assets, amounts invested, and portfolio diversification. The effects are larger for males and for residents of urban regions. Causal mediation analysis suggests that increased financial literacy and the decline in Confucian norms of filial piety are the potential channels of transmission through which education affects household financial behavior. The results highlight the importance of educational, social, and market reform in a sui generis environment of limited household participation in financial markets. The second essay investigates the effect of early life exposure to local financial markets using the reform of special economic zones and coastal cities (SEZ) in China that led to differential development of financial markets across Chinese cities. I find that individuals who were still at school during the time and after the reforms are more likely to access finance from formal financial institutions, compared to a control group of individuals born in non-SEZ regions and those who were at post-schooling age during the reforms. Those exposed to local financial institutions early in life are less likely to obtain finance from informal sources and have lower informal-to-total finance ratios. Using difference-indifference estimation, I find a large significant impact of growing up with finance on financial market participation, in terms of stock and risky-asset ownership and holdings, as well as on portfolio diversification. The effects are stronger for individuals who grew up and currently live in SEZ regions, compared to those who moved there from other parts of China. The inquiry suggests that higher financial literacy mediates the effect of early life exposure to financial institutions among individuals living in SEZ regions. The mediating effect is higher than that of financial risk tolerance, peer effects on social interactions, and filial piety, inter alia. The third essay examines the relationship between financial inclusion and happiness in China, I find large effects of financial inclusion on subjective well-being, and those effects are robust to specifications with regional macroeconomic indicators and relative income as well as the usage of proxies for formal and informal finance. The instrumental-variable estimates suggest that the financially included are 10% -20% happier on average, with the financially excluded being 30% - 40% more likely to be unhappy. Causal mediation analysis suggests that financial resilience, in terms of higher liquid-asset ownership rate, is the channel that explains that relationship between financial inclusion and well-being in China

Explainable fault prediction using learning fuzzy cognitive maps

IoT sensors capture different aspects of the environment and generate high throughput data streams. Besides capturing these data streams and reporting the monitoring information, there is significant potential for adopting deep learning to identify valuable insights for predictive preventive maintenance. One specific class of applications involves using Long Short-Term Memory Networks (LSTMs) to predict faults happening in the near future. However, despite their remarkable performance, LSTMs can be very opaque. This paper deals with this issue by applying Learning Fuzzy Cognitive Maps (LFCMs) for developing simplified auxiliary models that can provide greater transparency. An LSTM model for predicting faults of industrial bearings based on readings from vibration sensors is developed to evaluate the idea. An LFCM is then used to imitate the performance of the baseline LSTM model. Through static and dynamic analyses, we demonstrate that LFCM can highlight (i) which members in a sequence of readings contribute to the prediction result and (ii) which values could be controlled to prevent possible faults. Moreover, we compare LFCM with state-of-the-art methods reported in the literature, including decision trees and SHAP values. The experiments show that LFCM offers some advantages over these methods. Moreover, LFCM, by conducting a what-if analysis, could provide more information about the black-box model. To the best of our knowledge, this is the first time LFCMs have been used to simplify a deep learning model to offer greater explainability

The Definition and Choice of Environmental Commodities for Nonmarket Valuation

Economic analyses of nature must somehow define the “environmental commodities” to which values are attached. This paper articulates a set of principles to guide the choice and interpretation of nonmarket commodities. We describe how complex natural systems can be decomposed consistent with what can be called “ecological production theory.” Ecological production theory--like conventional production theory--distinguishes between biophysical inputs, process, and outputs. We argue that a systems approach to the decomposition and presentation of natural commodities can inform and possibly improve the validity of nonmarket environmental valuation studies. We raise concerns about the interpretation, usefulness, and accuracy of benefit estimates derived without reference to ecological production theory.nonmarket valuation, stated preference, revealed preference, commodities, endpoints