Learning and adaptive management mechanisms in implementing development programs in Zaka district, Zimbabwe

The nature and peculiarities of formation of risks of economic activity of enterprise are researched. Expediency of application of instruments of adaptive risk management of economic activity of enterprise on the basis of use of the situational approach and study of the risk management system in dynamics is substantiated. A model of adaptive risk management of economic activity of enterprise on the basis of precedents has been formed, which includes subsystems of active and reactive risk management. Important components of influence on the adaptive management system are the current risk situation caused by the existing risk factors, the dynamics of risk factors development and the strategy of risk management of economic activity adopted by the enterprise, which defines the targets, priorities and prerequisites for making adaptive management decisions at the enterprise in the face of risk. The accumulation of information about the object of management within this model allows developing optimal management decisions, accumulating information about adaptive decisions in the Bank of precedents of management decisions and using them as a template for making management decisions in similar (typical) situations. It is substantiated that an important feature of the development of the system of adaptive management of economic risks at enterprise is the presence of a certain direction of transformation - the vector of development of the adaptive management system. A vector model of adaptive transformation of the risk management system at enterprise has been proposed, which contributes to a comprehensive understanding of the process of development of the system of adaptive risk management of economic activity of enterprise. Building a model of risk management at enterprise on the basis of precedents, taking into account the vector nature of its development, will contribute to a holistic understanding of the patterns of formation and transformation of the system of adaptive risk management of economic activity at enterprise. The study substantiates the feasibility of using an adaptive approach in the system of enterprise economic risk management, which is considered as a flexible open system capable of self-learning and progressive development. The use of precedents for making adaptive management decisions increases the system's ability to self-develop and more effectively adapt to the changing conditions of the external and internal environment of the enterprise.

We are entering an era where species declines are occurring at their fastest ever rate, and the increased spread of non‐native species is among the top causes. High uncertainty in biological processes makes the accurate prediction of the outcomes of management interventions very challenging. Adaptive management (AM) offers solutions to reduce uncertainty and improve predictability so that the outcomes of interventions can continuously improve. We quantitatively assess the extent to which AM is used for managing vertebrates, with a focus on invasive non‐native species (INNS). Using the Web of Science, we evaluated 3992 articles returned by the search terms ‘adaptive management’ or ‘adaptive harvest management’ against seven recommended elements of AM (engagement with stakeholders, defining objectives, forecasting and estimating uncertainty, implementing management, monitoring populations, adjusting management in response to monitoring, and improving forecasting and reducing uncertainty in response to monitoring populations). The use of AM for vertebrates was reported in 56 (1%) of the evaluated studies; including four for managing INNS. Of these, ten studies excluding INNS and no studies of INNS management implemented all seven recommended elements of AM. Those elements infrequently implemented were as follows: the use of analysis or models to forecast and represent uncertainty (44%) and the feedback of monitoring data to improve forecasting and reduce uncertainty (25%). Complete active AM has rarely been implemented and reported for managing INNS, despite the significant advantages it offers. Among studies purporting to have implemented AM, most did not use analyses or models to forecast and represent uncertainty, while most defined objectives, implemented management, and monitored populations. Improvements to ongoing control programmes and much broader adoption of the AM approach are required to increase the efficiency and success of INNS management campaigns and reduce their negative impacts on native species.

This paper addresses issues on adaptive water management under the impact of climate change. Based on a set of comprehensive indicators of water system, a decision making approach of multi-objects is developed and applied to quantify water adaptive management for the demands of water sustainable use, water environmental protection and eco-water requirement under the climate change. For this study in China, two key indicators are proposed, namely (1) the water resources vulnerability (V) that was represented by integrated sensitivity (S) and resilience (C) of climate change impact on water resources, and (2) the sustainability of socio-economy and water environment, marked by DD, that is integrated scaler of socio-economic development (EG) based on the amount of GDP and the water environment and relative eco-system quality (LI). To find a reasonable solution for adaptive water management, a multi-objective decision making model of adaptive water management is further developed and the multi-objective model was transformed into an integrated single optimization model through developing an integrated measure function, called as VDD=DD/V. This approach has been applied to adaptive water resources planning and management for case study of China with new policy, called as the strict management of water resources based on three red line controls, i.e., the control of total water use by the total water resources allocation, the control of lower water use efficiency by the water demand management and the control of the total waste water load by water quality management in the Eastern China Monsoon Region that covers major eight big river basins including Yangtze River, Yellow River, Haihe River and Huaihe River. It is shown that the synthetic representation of water resource vulnerability and socio-economic sustainability by the integrated objective function (VDD) and integrated decision making model are workable and practicable. Adaptive management effect of the criterion compliance rate and water use efficiency are more appreciable through new water policy of the three red line controls, which can reduce 21.3% of the water resources vulnerability (V) and increase 18.4% of the sustainability of socioeconomy and water environment (DD) for the unfavorable scenario of climate change in 2030.

Risk management in finance is an increasingly important area, which has been highlighted in the midst of the global credit crisis. In this paper, we propose a new risk management model for pension investment funds: a risk management framework and risk measure based on modelling the pension fund value as an option. We review risk management, discussing risk management frameworks and risk management measures. We discuss the advantages of our option-based risk management model and framework and conduct numerical experiments on our pension fund portfolio. We show that our risk management framework and model provides advantageous insight into managing pension funds.

The study explores adaptive enterprise management as a strategic framework for maintaining optimal efficiency in dynamic and uncertain environments. It was stated that modern technological platforms play a dual role, either accelerating or constraining adaptive management processes. On one hand, traditional data distribution across departments can limit real-time visibility required for timely and accurate economic judgments. On the other hand, cloud-based infrastructures with integrated data repositories empower managers to analyze aggregated indicators swiftly and adjust strategies based on updated patterns. As was declared, adaptive management in contemporary organizations faces challenges stemming from the complexity of data sources, such as transactional databases, consumer feedback, operational logs, and financial reports. Subsequently, these diverse and increasingly sophisticated data sources necessitate advanced analytical methodologies capable of extracting meaningful insights from unstructured text, high-dimensional numerical data, and time-series signals reflecting system performance. Relational databases, widely used in transactional processing and resource management, often rely on numerical and categorical metrics like query execution times and error counts. However, these metrics fail to capture the broader semantic context of issues documented in textual logs and feedback. BERT embeddings transform raw textual inputs, such as error messages or policy documentation, into high-dimensional vectors retaining semantic detail. Simultaneously, LSTM networks manage temporal dependencies in performance trends, selectively retaining long-term patterns while discarding irrelevant fluctuations. The combined feature vectors—comprising BERT embeddings and numerical metrics—are fed into LSTM layers to model fluctuations in database performance dynamically. The research emphasizes the critical role of real-time analytics and adaptive decision-making facilitated by advanced information architectures. It highlights the economic value of integrating data-driven insights into adaptive enterprise management, enabling rapid optimization of processes and resource allocation. However, technological advancements alone are insufficient without organizational readiness to act on insights, balanced incentives, and a culture of adaptability. The study underscores the balance between centralized oversight for compliance and decentralized decision-making for local adaptability. It was concluded, that while centralization supports risk management standardization, excessive control can stifle frontline innovation. Conversely, excessive decentralization risks incoherence and inefficiency. This work proposes an economic model that integrates organizational structures and technological infrastructures to improve decision-making processes in real-time.

There is a growing focus on the need for proactive management of climate change risk in agriculture and agricultural technologies are deemed necessary for tackling climate adaptation and/or risk management issue in the sector. Thus, this study tries to identify the in-built crop production technology related adaptation and/or risk management strategies with smallholder farmers’ perception in moisture-stressed areas of the central rift valley of Ethiopia. Results of descriptive analysis showed that high temperature, short rain and pests (indirectly) have been the most important phenomena of climate change causing loss of crop production, exhaustion and illness. Also, smallholder farmers’ adaptation and/or risk management strategies were found to be many and were usually composed of traditional and improved practices. According to farmers, the improved varieties performed from less to higher susceptibility to climate change. Results of the multivariate probit model revealed that farmers’ adaptation and/or risk management strategies are governed by different sets of socioeconomic and climatic factors which could be summarized as farmer-specific, technology specific and environment specific. Therefore, it is recommended that policy actions should consider the impact of climate change and the characteristics and diversity of adaptation and/or risk management strategies within the framework of the different socio-economic and local settings influencing farmers’ decision. Keywords: Adaptation and/or risk management mechanisms, farmers’ decision making behavior DOI : 10.7176/JEES/9-4-04 Publication date : April 30 th 2019

Adaptive management is the problem-solving approach of choice proposed for complex and multistakeholder environments, which are, at best, only partly predictable. We discuss the implications of this approach as applicable to scientists, who have to overcome certain entrained behaviour patterns in order to participate effectively in an adaptive management process. The challenge does not end there. Scientists and managers soon discover that an adaptive management approach does not only challenge conventional scientific and management behaviour but also clashes with contemporary organisational culture. We explore the shortcomings and requirements of organisations with regard to enabling adaptive management. Our overall conclusion relates to whether organisations are learning-centred or not. Do we continue to filter out unfamiliar information which does not fit our world view and avoid situations where we might fail, or do we use new and challenging situations to reframe the question and prepare ourselves for continued learning? Conservation implications: For an organisation to effectively embrace adaptive management, its mangers and scientists may first have to adapt their own beliefs regarding their respective roles. Instead of seeking certainty for guiding decisions, managers and scientists should acknowledge a degree of uncertainty inherent to complex social and ecological systems and seek to learn from the patterns emerging from every decision and action. The required organisational culture is one of ongoing and purposeful learning with all relevant stakeholders. Such a learning culture is often talked about but rarely practised in the organisational environment.

Chapter 5 - Influence of global climate change on water resources in South Africa: toward an adaptive management approach

As population increases, one of the factors affecting life is traffic. Efficient traffic management has a direct positive impact on issues such as time, carbon dioxide emissions, and fuel consumption. Today, an important parameter under the heading of traffic is the signalling systems for intersections, which are operated with fixed-time, semi-actuated, fully actuated, and fully adaptive control methods. In this study, an adaptive traffic management model is developed for signalised intersections. The adaptive traffic management model developed includes phase extension with minimum and maximum time intervals dependent on density and phase skip features. Additionally, the most distinctive feature of the model is its flexible phase structure rather than a sequential phase. The Heybe intersection, located within the boundaries of Antalya province, is modelled one-to-one in the simulation of urban mobility (SUMO) simulation programme with real intersection data. The developed adaptive traffic management model is applied to the Heybe intersection, and the effects of the model are revealed. Improvements obtained from the SUMO simulation programme were verified through visual inspection, and high-accuracy results were determined. As a result of the studies, it was found that the application of the adaptive traffic management model developed at Heybe intersection, which has approximately 50,000 vehicles passing daily, resulted in a 27.2 % improvement in the average delay per vehicle parameter, a 32.4 % improvement in the average waiting time per vehicle parameter, and a 16.7 % improvement in the average speed per vehicle parameter.

SUMMARYIn recent years, the term ‘adaptive management’ has become integrated in government agriculture and conservation programmes in the United States. Adaptive management is described as ‘a process of developing improved management practices for efficient production and resource conservation by use of participatory learning through continuous systematic assessment’, by the NEERA1002 Coordinating Committee on Adaptive Nutrient Management. Such developments reflect recognition by government agencies of (1) the need for continuous improvement in managing agricultural systems to enhance productivity and environmental protection at farm, state, watershed and federal levels, and (2) the effectiveness of continuous systematic assessment and an outcome-based evaluation in achieving and documenting such progress. Various methods have existed for knowledge development and transfer in the past, some effective, others less successful. The present paper presents and discusses lessons learned from three different adaptive management approaches, implemented at field, whole farm and regional/state levels, which contributed to improvements in field, farm and regional/state balances for nitrogen and phosphorus in New York. These examples show that a combination of governmental regulations and an innovative, outcome-focused adaptive management approach that includes research, extension, and a focus on human dimensions (a people-based approach) will be most effective in obtaining greater sustainability of agriculture in future years.

The Monitoring and Evaluation (M&E) Department of KSrelief developed many monitoring, evaluation and learning policies, processes, guidelines, and tools between 2019 to 2021 to promote learning and adaptive programming. During the same period, these policies, processes, guidelines, and tools have been adopted by 26 implementing partners across 37 projects in 13 countries. The findings from all 37 M&E reports were analyzed to assess their effectiveness as well as to identify the enabling factors that promoted learning and adaptive management. The results show that an easy-to-use MEL system is crucial for learning to adaptive programming. Supportive organizational structures and processes are needed for a smooth and collaborative flow of learning. The lesson learned is that to make the learning processes visible, meaningful, and effective the whole process should be planned and documented at the project design phase and shared with all stakeholders throughout the project’s cycle. Continuous reflection and analysis can enable the organization to learn from their own successes and failures, as well as from the various factors that hinder or facilitate a project’s performance review. For adaptive management, the project leadership team on the ground must have decision making authority and the implementation plan must be flexible to respond to changing conditions.

IntroductionFederal agencies are increasingly expected to adopt enterprise risk management (ERM). However, public sector adoption of ERM has typically focused on the economic efficiency of tax-financed activities based on control-based practices. This reflects an emphasis on quantifiable concerns that invariably directs attention to risk, that (by definition) relates to identifiable and measurable events, thereby downplaying uncertain and unknown aspects of public exposures. This is a potentially serious shortcoming as government entities often act as society's risk managers of last resort. When extreme events happen what were previously considered private matters can quickly turn into public obligations. Hence, there is a need for proactive assessments of the evolving public risk landscape to discern unpredictable-even unknowable-developments.MethodsThe article reviews recent empirical studies on public risk management practices, effects of digitalization in public sector institutions, current strategic management research, and insights uncovered from a recent study of risk management practices in federal agencies. On this basis, the article explains how the ability to generate value from ERM can be enhanced when it intertwines with local responsive initiatives and central strategic risk analyses. It can form a dynamic adaptive risk management process where insights from dispersed actors inform updated risk analyses based on local autonomy and open exchange of information. This approach builds on specific structural features embedded in culture-driven aspirations to generate collaborative solutions. Its functional mode is an interactive control system with open discussions across levels and functions in contrast to conventional diagnostic controls that monitor predetermined key performance indicators (KPIs) and key risk indicators (KRIs).FindingsBacked by theoretical rationales and empirical research evidence, it is found that applications of ERM frameworks can produce positive results but is unable to deal with a public risk landscape characterized by uncertain unpredictable conditions with potentially extreme outcome effects. It is shown how interactive exchange of fast local insights and slow integrated strategic risk analyses supported by digitized data processing can form a dynamic adaptive system that enable public sector institutions to deal with emergent high-scale exposures. It is explained how the requirement for conducive organizational structures and supportive values require a new strategic risk leadership approach, which is contrasted to observed practices in federal agencies that are constrained by prevailing public governance requirements.DiscussionThe need to deal with uncertainty and unknown conditions demands a cognitive shift in current thinking from a primary focus on risk to also appraise complexity and prepare for the unexpected where data-driven methods can uncover emergent exposures through dynamic information processing. This requires strategic risk leaders that recognize the significance of complex public exposures with many unknowns and a willingness to facilitate digitalized information processing rooted in a collaborative organizational climate. If handled properly, adoption of ERM in public risk management can consider emergent dimensions in complex public exposures applying interactive information processing as a dynamic adaptive risk management approach incorporating digitized methods to solicit collective intelligence for strategic risk updating.

A conceptual modeling of an adaptive or a flexible learning content management (LCM) system is an initialized step toward successful implement of a distance education system on Internet. Its aim is at planning and accomplishing the responsiveness to the learning content (re)configuration required for meeting the students' learning goals. Since learning content is composed of the relative learning objects, their (re)configurations and reuses are essential to the construction (modification) of a new (existing) studying subject (content). The alternatives of appropriately aggregating a number of relative learning objects to structure the learning subject certainly is to the success of the LCM system implementation. This paper thus presents such a conceptualization that is able to model an adaptive LCM system by defining its learning object constituents as the respectively reusable active objects represented in the nature of the object-orientation (OO) software. The aspect of the proposed OO-based active objects instruments the modeling process of an adaptive LCM system.

The increasing complexity and dynamism of cloud environments have introduced significant cybersecurity challenges, necessitating advanced risk assessment methodologies capable of handling uncertainty and evolving threats. Traditional risk management frameworks often rely on static and rule-based mechanisms, which lack adaptability in dynamic cloud ecosystems. To address these limitations, this chapter explores the integration of fuzzy logic and evolutionary computation for adaptive cyber risk management in cloud environments. Fuzzy logic provides a powerful framework for modeling imprecise security parameters and uncertainty in threat landscapes, enabling more flexible and context-aware risk assessment. Meanwhile, evolutionary computation offers an adaptive mechanism to optimize cybersecurity strategies through heuristic learning and intelligent decision-making. The chapter presents a hybrid risk assessment framework that leverages fuzzy inference systems to quantify risk levels and evolutionary algorithms to dynamically optimize security controls, it examines the scalability of fuzzy-evolutionary approaches in large-scale cloud infrastructures and their effectiveness in mitigating real-time cyber threats, such as zero-day attacks, insider threats, and advanced persistent threats. The potential integration of explainable AI (XAI), deep learning, and quantum computing in enhancing fuzzy-based risk assessment models is also discussed. This research contributes to the advancement of self-learning, adaptive cyber defense mechanisms capable of proactively mitigating risks in multi-cloud and hybrid-cloud environments. The proposed framework ensures improved threat intelligence, automated risk prioritization, and enhanced decision transparency, offering a robust solution for next-generation cloud security.

This report contains a summary of activities of Gnomon, Inc. and five sub-contractors that have taken place during the first six months (January 1, 2003--June 30, 2003) under the DOE-NETL cooperative agreement: ''Adaptive Management and Planning Models for Cultural Resources in Oil & Gas Fields in New Mexico and Wyoming'', DE-FC26-02NT15445. Gnomon, Inc. and all five (5) subcontractors have agreed on a process for the framework of this two-year project. They have also started gathering geomorphological information and entering cultural resource data into databases that will be used to create models later in the project. This data is being gathered in both the Power River Basin of Wyoming, and the Southeastern region of New Mexico. Several meetings were held with key players in this project to explain the purpose of the research, to obtain feedback and to gain support. All activities have been accomplished on time and within budget with no major setbacks.