arXiv subjects

Uygar Ozesmi

Publications and source records attributed to Uygar Ozesmi.

11 recordsLinked to original sources

arXiv2019

The Prosumer Economy -- Being Like a Forest

Planetary life support systems are collapsing due to climate change and the biodiversity crisis. The root cause is the existing consumer economy, coupled with profit maximisation based on ecological and social externalities. Trends can be reversed, civilisation may be saved by transforming the profit maximising consumer economy into an ecologically and socially just economy, which we call the prosumer economy. Prosumer economy is a macro scale circular economy with minimum negative or positive ecological and social impact, an ecosystem of producers and prosumers, who have synergistic and circular relationships with deepened circular supply chains, networks, where leakage of wealth out of the system is minimised. In a prosumer economy there is no waste, no lasting negative impacts on the ecology and no social exploitation. The prosumer economy is like a lake or a forest, an economic ecosystem that is productive and supportive of the planet. We are already planting this forest through Good4Trust.org, started in Turkey. Good4Trust is a community platform bringing together ecologically and socially just producers and prosumers. Prosumers come together around a basic ethical tenet the golden rule and share on the platform their good deeds. The relationship are already deepening and circularity is forming to create a prosumer economy. The platforms software to structure the economy is open source, and is available to be licenced to start Good4Trust anywhere on the planet. Complexity theory tells us that if enough agents in a given system adopt simple rules which they all follow, the system may shift. The shift from a consumer economy to a prosumer economy has already started, the future is either ecologically and socially just or bust.

econ.GN↗

arXiv2006

Cognitive Maps of Complex Systems Show Hierarchical Structure and Scale-Free Properties

Many networks in natural and human-made systems exhibit scale-free properties and are small worlds. Now we show that people's understanding of complex systems in their cognitive maps also follow a scale-free topology (P_k = k^-lambda, lambda [1.24,3.03]; r^2 <= 0.95). People focus on a few attributes, as indicated by a fat tail in the probability distribution of total degree. These few attributes are related with many other variables in the system. Many more attributes have very few connections. The scale-free properties in the cognitive maps of people arise despite the fact that their average distances are not different (Wilcoxon sign-rank test, W=78, p=0.75) than random networks of the same size and connection density. The scale-free property manifests itself in the higher hierarchical structure compared to random networks (Wilcoxon sign-rank test, W=12, p=0.03). People use relatively short explanations to describe systems. These findings may help us to better understand people's perceptions, especially when it comes to decision-making, conflict resolution, politics and management.

q-bio.NC↗

arXiv2006

Ecosystems in the Mind: Fuzzy Cognitive Maps of the Kizilirmak Delta Wetlands in Turkey

Sustainability of ecosystems & ecosystem management are increasingly accepted societal goals. Can conservation programs be improved by incorporating local people's understanding of ecosystems? The Kizilirmak Delta is one of Turkey's most important wetland complexes. It is also one of the most productive agricultural deltas in Turkey. We obtained 31 cognitive models of the social & ecological system. These models were converted to adjacency matrices, analyzed using graph theoretical methods, & augmented into social cognitive maps. Causal "What-if" scenarios were run to determine the trajectory of the ecosystem based on models defined by stakeholders. Villagers had significantly larger numbers of variables, more complex maps, a broader understanding of all the variables that affect the Kizilirmak Delta, & mentioned more variables that control the ecosystem than did NGO and government officials. Villagers adapting to changing ecological & social conditions actively changed & challenged conditions through the political process. Villagers were faced with many important forcing functions that they could not control. Most of the variables defined by villagers were related to agriculture and animal husbandry. Conservation policies & ecosystem management must encompass larger environmental issues & villagers' cognitive maps must be reconciled with that of NGOs & government officials. Cognitive maps can serve as a basis for discussion when policies & management options are formulated. A villager-centered cognitive mapping approach is not only necessary because villagers resist conservation projects, or because top down projects that do not take local knowledge systems into account fail, but because it is the ethical and responsible way of doing ecosystem management.

q-bio.NC↗

arXiv2006

Fuzzy Cognitive Maps Of Local People Impacted By Dam Construction: Their Demands Regarding Resettlement

Fuzzy cognitive mapping was used to understand the wants and desires of local people before resettlement. Variables that the affected people think will increase their welfare during and after dam construction were determined. Simulations were done with their cumulative social cognitive map to determine which policy options would most increase their welfare. The construction of roads, job opportunities, advance payment of condemnation value, and schools are central variables that had the most effect on increasing people's income and welfare. The synergistic effects of variables demonstrated that the implementation of different policies not only add cumulatively to the people's welfare but also have an increased effect.

q-bio.NC↗

arXiv2006

Perceptions of Complex Systems Are Governed by Power Laws

Many networks in natural and human-made systems exhibit scale-free properties and are small worlds. Now we show that people's understanding of complex systems in their cognitive maps also follow a scale-free topology. People focus on a few attributes, relating these with many other things in the system. Many more attributes have very few connections. People use relatively short explanations to describe events; their cognitive map is a small world with less than six degrees of separation. These findings may help us to better understand people's perceptions, especially when it comes to decision-making, conflict resolution, politics and management.

q-bio.OT↗

arXiv2005

Statistical Predictive Models in Ecology: Comparison of Performances and Assessment of Applicability

Ecological systems are governed by complex interactions which are mainly nonlinear. In order to capture this complexity and nonlinearity, statistical models recently gained popularity. However, although these models are commonly applied in ecology, there are no studies to date aiming to assess the applicability and performance. We provide an overview for nature of the wide range of the data sets and predictive variables, from both aquatic and terrestrial ecosystems with different scales of time-dependent dynamics, and the applicability and robustness of predictive modeling methods on such data sets by comparing different statistical modeling approaches. The methods considered k-NN, LDA, QDA, generalized linear models (GLM) feedforward multilayer backpropagation networks and pseudo-supervised network ARTMAP. For ecosystems involving time-dependent dynamics and periodicities whose frequency are possibly less than the time scale of the data considered, GLM and connectionist neural network models appear to be most suitable and robust, provided that a predictive variable reflecting these time-dependent dynamics included in the model either implicitly or explicitly. For spatial data, which does not include any time-dependence comparable to the time scale covered by the data, on the other hand, neighborhood based methods such as k-NN and ARTMAP proved to be more robust than other methods considered in this study. In addition, for predictive modeling purposes, first a suitable, computationally inexpensive method should be applied to the problem at hand a good predictive performance of which would render the computational cost and efforts associated with complex variants unnecessary.

q-bio.QM↗

arXiv2005

Methodological Issues in Building, Training, and Testing Artificial Neural Networks

We review the use of artificial neural networks, particularly the feedforward multilayer perceptron with back-propagation for training (MLP), in ecological modelling. Overtraining on data or giving vague references to how it was avoided is the major problem. Various methods can be used to determine when to stop training in artificial neural networks: 1) early stopping based on cross-validation, 2) stopping after a analyst defined error is reached or after the error levels off, 3) use of a test data set. We do not recommend the third method as the test data set is then not independent of model development. Many studies used the testing data to optimize the model and training. Although this method may give the best model for that set of data it does not give generalizability or improve understanding of the study system. The importance of an independent data set cannot be overemphasized as we found dramatic differences in model accuracy assessed with prediction accuracy on the training data set, as estimated with bootstrapping, and from use of an independent data set. The comparison of the artificial neural network with a general linear model (GLM) as a standard procedure is recommended because a GLM may perform as well or better than the MLP. MLP models should not be treated as black box models but instead techniques such as sensitivity analyses, input variable relevances, neural interpretation diagrams, randomization tests, and partial derivatives should be used to make the model more transparent, and further our ecological understanding which is an important goal of the modelling process. Based on our experience we discuss how to build a MLP model and how to optimize the parameters and architecture.

q-bio.PE↗

arXiv2005

Predictive Models for Characterization of Ecological Data

Although ARTMAP and ART-based models were introduced in early 70's they were not used in characterizing and classifying ecological observations. ART-based models have been extensively used for classification models based on satellite imagery. This report, to our knowledge, is the first application of ART-based methods and specifically ARTMAP for predicting habitat selection and spatial distribution of species. We compare the performance of ARTMAP to assess the breeding success of three bird species (Lanius senator, Hippolais pallida, and Calandrella brachydactyla) based on multi-spectral satellite imagery and environmental variables. ARTMAP is superior both in terms of performance (percent correctly classified - pcc = 1.00) and generalizability (pcc >0.96) to those of feedforward multilayer backpropogation (>0.87, >0.65), linear and quadratic discriminant analysis (>0.48, >0.46) and k-nearest neighbor (>0.82, >0.66) methods. Compared to other methods, ARTMAP is able to incorporate new observations with far less computational effort and can easily add data to already trained models.

q-bio.QM↗

arXiv2005

Participatory Ecosystem Management Planning at Tuzla Lake (Turkey) Using Fuzzy Cognitive Mapping

A participatory environmental management plan was prepared for Tuzla Lake, Turkey. Fuzzy cognitive mapping approach was used to obtain stakeholder views and desires. Cognitive maps were prepared with 44 stakeholders (villagers, local decisionmakers, government and non-government organization (NGO) officials). Graph theory indices, statistical methods and "What-if" simulations were used in the analysis. The most mentioned variables were livelihood, agriculture and animal husbandry. The most central variable was agriculture for local people (villagers and local decisionmakers) and education for NGO & Government officials. All the stakeholders agreed that livelihood was increased by agriculture and animal husbandry while hunting decreased birds and wildlife. Although local people focused on their livelihoods, NGO & Government officials focused on conservation of Tuzla Lake and education of local people. Stakeholders indicated that the conservation status of Tuzla Lake should be strengthened to conserve the ecosystem and biodiversity, which may be negatively impacted by agriculture and irrigation. Stakeholders mentioned salt extraction, ecotourism, and carpet weaving as alternative economic activities. Cognitive mapping provided an effective tool for the inclusion of the stakeholders' views and ensured initial participation in environmental planning and policy making.

q-bio.NC↗

arXiv2005

Generalizability of Artificial Neural Network Models in Ecological Applications: Predicting Nest Occurrence and Breeding Success of the Red-winged Blackbird Agelaius phoeniceus

Separate artificial neural network (ANN) models were developed from data in two geographical regions and years apart for a marsh-nesting bird, the red-winged blackbird Agelaius phoeniceus. Each model was independently tested on the spatially and temporally distinct data from the other region to determine how generalizable it was. The first model was developed to predict occurrence of nests in two wetlands on Lake Erie, Ohio in 1995 and 1996. The second model was developed to predict breeding success in two marshes in Connecticut, USA in 1969 and 1970. Independent variables were vegetation durability, stem density, stem/nest height, distance to open water, distance to edge, and water depth. With input variable relevances, sensitivity analyses and neural interpretation diagrams we were able to understand how the different models predicted nest occurrence and breeding success and compare their differences and similarities. Both models also predicted increasing nest occurrence/breeding success with increasing water depth under the nest and increasing distance to edge. However, relationships for prediction differed in the models. Generalizability of the models was poor except when the marshes had similar values of important variables in the model. ANN models performed better than generalized linear models (GLM) on marshes with similar structures. Generalizability of the models did not differ in nest occurrence and breeding success data. Extensive testing also showed that the GLMs were not necessarily more generalizable than ANNs, suggesting that ANN models make good definitions of a study system but are too specific to generalize well to other ecologically complex systems unless input variable distributions are very similar.

q-bio.PE↗

arXiv2005

A Cognitive Model of an Epistemic Community: Mapping the Dynamics of Shallow Lake Ecosystems

We used fuzzy cognitive mapping (FCM) to develop a generic shallow lake ecosystem model by augmenting the individual cognitive maps drawn by 8 scientists working in the area of shallow lake ecology. We calculated graph theoretical indices of the individual cognitive maps and the collective cognitive map produced by augmentation. The graph theoretical indices revealed internal cycles showing non-linear dynamics in the shallow lake ecosystem. The ecological processes were organized democratically without a top-down hierarchical structure. The steady state condition of the generic model was a characteristic turbid shallow lake ecosystem since there were no dynamic environmental changes that could cause shifts between a turbid and a clearwater state, and the generic model indicated that only a dynamic disturbance regime could maintain the clearwater state. The model developed herein captured the empirical behavior of shallow lakes, and contained the basic model of the Alternative Stable States Theory. In addition, our model expanded the basic model by quantifying the relative effects of connections and by extending it. In our expanded model we ran 4 simulations: harvesting submerged plants, nutrient reduction, fish removal without nutrient reduction, and biomanipulation. Only biomanipulation, which included fish removal and nutrient reduction, had the potential to shift the turbid state into clearwater state. The structure and relationships in the generic model as well as the outcomes of the management simulations were supported by actual field studies in shallow lake ecosystems. Thus, fuzzy cognitive mapping methodology enabled us to understand the complex structure of shallow lake ecosystems as a whole and obtain a valid generic model based on tacit knowledge of experts in the field.

q-bio.NC↗