Coping with Climatic Variability by Rain-fed Farmers in Dry Zone, Sri Lanka: Towards Understanding Adaptation to Climate Change

Climate change introduces numerous uncertainties over the livelihoods of farming communities that depend heavily on weather and climate. Rain-fed farmers in developing countries are among the most vulnerable communities. However, climate risks are not new to farmers. Coping with ‘natural variability’ of climate has been a constant challenge faced by farmers even though broad sweeping change in climate due to anthropogenic causes is a relatively new prospect. Some argue ‘climate change’ could be significantly different from ‘climatic variability’ known to and experienced by farmers. In spite of this it is widely accepted that understanding farmers’ behavior towards adapting to climatic variability could generate useful insights in facing the risk of climate change. In Sri Lanka, the village tank farming community in the dry zone is one of the most vulnerable communities thereby deserving the priority attention of policy makers. This study is based on information gathered in Anuradhapura district of Sri Lanka. It depends mainly on information from secondary sources supplemented by qualitative primary information. Analysis was guided by recently introduced behavioral economics concepts of decisions based on experience. Accordingly adaptation is viewed as a response to the climate perceptions of farmers' aided by judgments based on heuristics. Farmers' adaptation decisions can be explained on the basis of their perception of climate variability with two major components. Firstly, farmers perceive climatic variability as an average annual pattern with variable probabilities of seasonal distribution of precipitation. Farmers base their long-term adaptation responses on this perceived average pattern and many of the choices made by them in the existing farming system and resource management practices can be explained accordingly. The average pattern of variability is largely a shared perception and therefore enables the option of joint adaptation. The land allocation practice popularly known as ‘Bethma’ provides a fine example for this. Secondly, farmers also perceive feasibility of random shocks with variable probabilities across the average pattern. This gives rise to short-term responses in the farming system activities. Such responses seem to be more individually oriented and reflect the variations in individual perceptions of climate risks.Adaptation, Climatic variability, Village tanks, Climate change, Rain-fed farmer, Environmental Economics and Policy,

A needs theory of governance

New-institutional economics hypothesizes imperfect rationality, self-seeking preferences, monetary-related needs, and opportunism as fundamental features of human behavior. Consistently, new-institutionalist models of governance highlight the efficiency and transaction costs minimizing features of control rights and governance. Differently, needs theory of governance, as here presented, hypothesizes imperfect rationality, multiple needs, and reciprocity, in which case opportunism is reduced to an exception to individual behavior. Consistently, it presents a theory that links production governance with the wellbeing of those partaking in production. Building on Maslow’s human psychology, the governance model suggested in this paper is aimed at evidencing the self-actualization potential of control rights, organizational structures and practices. The application of Maslow’s theory to the institutional structure of organizations suggests that the deepest organizational layers (control rights and governance) broadly correspond to the most basic needs in Maslow’s theory (survival, security and belonging), while the outer layers (managerial models and employment relations) correspond to the fulfillment of the highest needs (self-esteem and self-actualization). Cooperative firms are used as an illustration of governance solutions consistent with needs theory in human psychology

A canonical theory of dynamic decision-making

Decision-making behavior is studied in many very different fields, from medicine and eco- nomics to psychology and neuroscience, with major contributions from mathematics and statistics, computer science, AI, and other technical disciplines. However the conceptual- ization of what decision-making is and methods for studying it vary greatly and this has resulted in fragmentation of the field. A theory that can accommodate various perspectives may facilitate interdisciplinary working. We present such a theory in which decision-making is articulated as a set of canonical functions that are sufficiently general to accommodate diverse viewpoints, yet sufficiently precise that they can be instantiated in different ways for specific theoretical or practical purposes. The canons cover the whole decision cycle, from the framing of a decision based on the goals, beliefs, and background knowledge of the decision-maker to the formulation of decision options, establishing preferences over them, and making commitments. Commitments can lead to the initiation of new decisions and any step in the cycle can incorporate reasoning about previous decisions and the rationales for them, and lead to revising or abandoning existing commitments. The theory situates decision-making with respect to other high-level cognitive capabilities like problem solving, planning, and collaborative decision-making. The canonical approach is assessed in three domains: cognitive and neuropsychology, artificial intelligence, and decision engineering

An Agent Based Model to Assess Crew Temporal Variability During U.S. Navy Shipboard Operations

Understanding the factors that affect human performance variability as well as their temporal impacts is an essential element in fully integrating and designing complex, adaptive environments. This understanding is particularly necessary for high stakes, time-critical routines such as those performed during nuclear reactor, air traffic control, and military operations. Over the last three decades significant efforts have emerged to demonstrate and apply a host of techniques to include Discrete Event Simulation, Bayesian Belief Networks, Neural Networks, and a multitude of existing software applications to provide relevant assessments of human task performance and temporal variability. The objective of this research was to design and develop a novel Agent Based Modeling and Simulation (ABMS) methodology to generate a timeline of work and assess impacts of crew temporal variability during U.S. Navy Small Boat Defense operations in littoral waters. The developed ABMS methodology included human performance models for six crew members (agents) as well as a threat craft, and incorporated varying levels of crew capability and task support. AnyLogic ABMS software was used to simultaneously provide detailed measures of individual sailor performance and of system-level emergent behavior. This methodology and these models were adapted and built to assure extensibility across a broad range of U.S. Navy shipboard operations. Application of the developed ABMS methodology effectively demonstrated a way to visualize and quantify impacts/uncertainties of human temporal variability on both workload and crew effectiveness during U.S. Navy shipboard operations