Communicative and Anticipatory Decision-Making Supported by Bayesian Networks

The purpose of this research is to draw up a clear construction of an anticipatory communicative decision-making process and a successful implementation of a Bayesian application that can be used as an anticipatory communicative decision-making support system. This study is a decision-oriented and constructive research project, and it includes examples of simulated situations. As a basis for further methodological discussion about different approaches to management research, in this research, a decision-oriented approach is used, which is based on mathematics and logic, and it is intended to develop problem solving methods. The approach is theoretical and characteristic of normative management science research. Also, the approach of this study is constructive. An essential part of the constructive approach is to tie the problem to its solution with theoretical knowledge. Firstly, the basic definitions and behaviours of an anticipatory management and managerial communication are provided. These descriptions include discussions of the research environment and formed management processes. These issues define and explain the background to further research. Secondly, it is processed to managerial communication and anticipatory decision-making based on preparation, problem solution, and solution search, which are also related to risk management analysis. After that, a solution to the decision-making support application is formed, using four different Bayesian methods, as follows: the Bayesian network, the influence diagram, the qualitative probabilistic network, and the time critical dynamic network. The purpose of the discussion is not to discuss different theories but to explain the theories which are being implemented. Finally, an application of Bayesian networks to the research problem is presented. The usefulness of the prepared model in examining a problem and the represented results of research is shown. The theoretical contribution includes definitions and a model of anticipatory decision-making. The main theoretical contribution of this study has been to develop a process for anticipatory decision-making that includes management with communication, problem-solving, and the improvement of knowledge. The practical contribution includes a Bayesian Decision Support Model, which is based on Bayesian influenced diagrams. The main contributions of this research are two developed processes, one for anticipatory decision-making, and the other to produce a model of a Bayesian network for anticipatory decision-making. In summary, this research contributes to decision-making support by being one of the few publicly available academic descriptions of the anticipatory decision support system, by representing a Bayesian model that is grounded on firm theoretical discussion, by publishing algorithms suitable for decision-making support, and by defining the idea of anticipatory decision-making for a parallel version. Finally, according to the results of research, an analysis of anticipatory management for planned decision-making is presented, which is based on observation of environment, analysis of weak signals, and alternatives to creative problem solving and communication.

Business intelligence viewpoint on managerial decision-making : Ill-structured problems and decision support systems

Vaikka liiketoimintatiedon hallintaa sekä johdon päätöksentekoa on tutkittu laajasti, näiden kahden käsitteen yhteisvaikutuksesta on olemassa hyvin rajallinen määrä tutkimustietoa. Tulevaisuudessa aiheen tärkeys korostuu, sillä olemassa olevan datan määrä kasvaa jatkuvasti. Yritykset tarvitsevat jatkossa yhä enemmän kyvykkyyksiä sekä resursseja, jotta sekä strukturoitua että strukturoimatonta tietoa voidaan hyödyntää lähteestä riippumatta. Nykyiset Business Intelligence -ratkaisut mahdollistavat tehokkaan liiketoimintatiedon hallinnan osana johdon päätöksentekoa. Aiemman kirjallisuuden pohjalta, tutkimuksen empiirinen osuus tunnistaa liiketoimintatiedon hyödyntämiseen liittyviä tekijöitä, jotka joko tukevat tai rajoittavat johdon päätöksentekoprosessia. Tutkimuksen teoreettinen osuus johdattaa lukijan tutkimusaiheeseen kirjallisuuskatsauksen avulla. Keskeisimmät tutkimukseen liittyvät käsitteet, kuten Business Intelligence ja johdon päätöksenteko, esitetään relevantin kirjallisuuden avulla – tämän lisäksi myös dataan liittyvät käsitteet analysoidaan tarkasti. Tutkimuksen empiirinen osuus rakentuu tutkimusteorian pohjalta. Tutkimuksen empiirisessä osuudessa paneudutaan tutkimusteemoihin käytännön esimerkein: kolmen tapaustutkimuksen avulla tutkitaan sekä kuvataan toisistaan irrallisia tapauksia. Jokainen tapaus kuvataan sekä analysoidaan teoriaan perustuvien väitteiden avulla – nämä väitteet ovat perusedellytyksiä menestyksekkäälle liiketoimintatiedon hyödyntämiseen perustuvalle päätöksenteolle. Tapaustutkimusten avulla alkuperäistä tutkimusongelmaa voidaan analysoida tarkasti huomioiden jo olemassa oleva tutkimustieto. Analyysin tulosten avulla myös yksittäisiä rajoitteita sekä mahdollistavia tekijöitä voidaan analysoida. Tulokset osoittavat, että rajoitteilla on vahvasti negatiivinen vaikutus päätöksentekoprosessin onnistumiseen. Toisaalta yritysjohto on tietoinen liiketoimintatiedon hallintaan liittyvistä positiivisista seurauksista, vaikka kaikkia mahdollisuuksia ei olisikaan hyödynnetty. Tutkimuksen merkittävin tulos esittelee viitekehyksen, jonka puitteissa johdon päätöksentekoprosesseja voidaan arvioida sekä analysoida. Despite the fact that the literature on Business Intelligence and managerial decision-making is extensive, relatively little effort has been made to research the relationship between them. This particular field of study has become important since the amount of data in the world is growing every second. Companies require capabilities and resources in order to utilize structured data and unstructured data from internal and external data sources. However, the present Business Intelligence technologies enable managers to utilize data effectively in decision-making. Based on the prior literature, the empirical part of the thesis identifies the enablers and constraints in computer-aided managerial decision-making process. In this thesis, the theoretical part provides a preliminary understanding about the research area through a literature review. The key concepts such as Business Intelligence and managerial decision-making are explored by reviewing the relevant literature. Additionally, different data sources as well as data forms are analyzed in further detail. All key concepts are taken into account when the empirical part is carried out. The empirical part obtains an understanding of the real world situation when it comes to the themes that were covered in the theoretical part. Three selected case companies are analyzed through those statements, which are considered as critical prerequisites for successful computer-aided managerial decision-making. The case study analysis, which is a part of the empirical part, enables the researcher to examine the relationship between Business Intelligence and managerial decision-making. Based on the findings of the case study analysis, the researcher identifies the enablers and constraints through the case study interviews. The findings indicate that the constraints have a highly negative influence on the decision-making process. In addition, the managers are aware of the positive implications that Business Intelligence has for decision-making, but all possibilities are not yet utilized. As a main result of this study, a data-driven framework for managerial decision-making is introduced. This framework can be used when the managerial decision-making processes are evaluated and analyzed.

Before internationalisation : reflections of lateral rigidity on managerial decision making

The purpose of the study is to analyse lateral rigidity in the framework of pre-internationalisation to find out its reflections on managerial decision making. The interest of the study lies in the intersection of the meaningful but relatively stagnant concept of lateral rigidity, and the pre-internationalisation phase of companies that has received only a limited amount of research attention. The theoretical basis for the study is drawn from managerial decision making and internationalisation literatures. Firstly, the study aims to define the concept of lateral rigidity in order to secondly find out how it influences managers’ pre-internationalisation decision making. The study is theoretical in nature, and is based solely on literature examination. Concept analysis method is used to determine the attributes of lateral rigidity for the purpose of recognising the concept in the pre-internationalisation framework. The attributes that are found to comprise lateral rigidity are culture, know-how, uncertainty and attitude. Furthermore, these attributes are more specifically found to consist of environmental, personal and operational matters. Through the analysis of the pre-internationalisation literature it is discovered that all the attributes appear there, and present a variety of influences on pre-internationalisation decision making that can be characterised as being negative. The study finds that culture influences managers’ decision making via subjective reasoning and behaviour that stem from a domestic inclination, and via unfamiliarity with foreign markets. Against assumption, home cultural factors, e.g. values and customs, do not appear to have an influence. Know-how is found to influence decision making via managers’ previous experiences, subjective abiding perceptions, and the usage of previous operation patterns. Uncertainty, then again, influences managers’ risk perception, unfamiliarity avoidance, and the scope of potential international operations. Attitude is found to have a robust influence on managerial decision making via the usage of familiar processes and decision regimes, subjective preference of convention, and plausible results of operations. Ergo, the effects of lateral rigidity on managers show to represent an encumbrance in the pre-internationalisation phase; even though internationalisation would take place, the related decisions and actions are highly constrained. Especially the subjectivity of managers is seen to have a meaningful role in the decision making process.

Fuzzy ontology for knowledge mobilisation and decision support

A growing concern for organisations is how they should deal with increasing amounts of collected data. With fierce competition and smaller margins, organisations that are able to fully realize the potential in the data they collect can gain an advantage over the competitors. It is almost impossible to avoid imprecision when processing large amounts of data. Still, many of the available information systems are not capable of handling imprecise data, even though it can offer various advantages. Expert knowledge stored as linguistic expressions is a good example of imprecise but valuable data, i.e. data that is hard to exactly pinpoint to a definitive value. There is an obvious concern among organisations on how this problem should be handled; finding new methods for processing and storing imprecise data are therefore a key issue. Additionally, it is equally important to show that tacit knowledge and imprecise data can be used with success, which encourages organisations to analyse their imprecise data. The objective of the research conducted was therefore to explore how fuzzy ontologies could facilitate the exploitation and mobilisation of tacit knowledge and imprecise data in organisational and operational decision making processes. The thesis introduces both practical and theoretical advances on how fuzzy logic, ontologies (fuzzy ontologies) and OWA operators can be utilized for different decision making problems. It is demonstrated how a fuzzy ontology can model tacit knowledge which was collected from wine connoisseurs. The approach can be generalised and applied also to other practically important problems, such as intrusion detection. Additionally, a fuzzy ontology is applied in a novel consensus model for group decision making. By combining the fuzzy ontology with Semantic Web affiliated techniques novel applications have been designed. These applications show how the mobilisation of knowledge can successfully utilize also imprecise data. An important part of decision making processes is undeniably aggregation, which in combination with a fuzzy ontology provides a promising basis for demonstrating the benefits that one can retrieve from handling imprecise data. The new aggregation operators defined in the thesis often provide new possibilities to handle imprecision and expert opinions. This is demonstrated through both theoretical examples and practical implementations. This thesis shows the benefits of utilizing all the available data one possess, including imprecise data. By combining the concept of fuzzy ontology with the Semantic Web movement, it aspires to show the corporate world and industry the benefits of embracing fuzzy ontologies and imprecision.

Adaptive Markov chain Monte Carlo and Bayesian filtering for state space models

This thesis is concerned with the state and parameter estimation in state space models. The estimation of states and parameters is an important task when mathematical modeling is applied to many different application areas such as the global positioning systems, target tracking, navigation, brain imaging, spread of infectious diseases, biological processes, telecommunications, audio signal processing, stochastic optimal control, machine learning, and physical systems. In Bayesian settings, the estimation of states or parameters amounts to computation of the posterior probability density function. Except for a very restricted number of models, it is impossible to compute this density function in a closed form. Hence, we need approximation methods. A state estimation problem involves estimating the states (latent variables) that are not directly observed in the output of the system. In this thesis, we use the Kalman filter, extended Kalman filter, Gauss–Hermite filters, and particle filters to estimate the states based on available measurements. Among these filters, particle filters are numerical methods for approximating the filtering distributions of non-linear non-Gaussian state space models via Monte Carlo. The performance of a particle filter heavily depends on the chosen importance distribution. For instance, inappropriate choice of the importance distribution can lead to the failure of convergence of the particle filter algorithm. In this thesis, we analyze the theoretical Lᵖ particle filter convergence with general importance distributions, where p ≥2 is an integer. A parameter estimation problem is considered with inferring the model parameters from measurements. For high-dimensional complex models, estimation of parameters can be done by Markov chain Monte Carlo (MCMC) methods. In its operation, the MCMC method requires the unnormalized posterior distribution of the parameters and a proposal distribution. In this thesis, we show how the posterior density function of the parameters of a state space model can be computed by filtering based methods, where the states are integrated out. This type of computation is then applied to estimate parameters of stochastic differential equations. Furthermore, we compute the partial derivatives of the log-posterior density function and use the hybrid Monte Carlo and scaled conjugate gradient methods to infer the parameters of stochastic differential equations. The computational efficiency of MCMC methods is highly depend on the chosen proposal distribution. A commonly used proposal distribution is Gaussian. In this kind of proposal, the covariance matrix must be well tuned. To tune it, adaptive MCMC methods can be used. In this thesis, we propose a new way of updating the covariance matrix using the variational Bayesian adaptive Kalman filter algorithm.

Decision making and decision support in service systems

This thesis is a literature study that develops a conceptual model of decision making and decision support in service systems. The study is related to the Ä-Logi, Intelligent Service Logic for Welfare Sector Services research project, and the objective of the study is to develop the necessary theoretical framework to enable further research based on the research project results and material. The study first examines the concepts of service and service systems, focusing on understanding the characteristics of service systems and their implications for decision making and decision support to provide the basis for the development of the conceptual model. Based on the identified service system characteristics, an integrated model of service systems is proposed that views service systems through a number of interrelated perspectives that each offer different, but complementary, implications on the nature of decision making and the requirements for decision support in service systems. Based on the model, it is proposed that different types of decision making contexts can be identified in service systems that may be dominated by different types of decision making processes and where different types of decision support may be required, depending on the characteristics of the decision making context and its decision making processes. The proposed conceptual model of decision making and decision support in service systems examines the characteristics of decision making contexts and processes in service systems, and their typical requirements for decision support. First, a characterization of different types of decision making contexts in service systems is proposed based on the Cynefin framework and the identified service system characteristics. Second, the nature of decision making processes in service systems is proposed to be dual, with both rational and naturalistic decision making processes existing in service systems, and having an important and complementary role in decision making in service systems. Finally, a characterization of typical requirements for decision support in service systems is proposed that examines the decision support requirements associated with different types of decision making processes in characteristically different types of decision making contexts. It is proposed that decision support for the decision making processes that are based on rational decision making can be based on organizational decision support models, while decision support for the decision making processes that are based on naturalistic decision making should be based on supporting the decision makers’ situation awareness and facilitating the development of their tacit knowledge of the system and its tasks. Based on the proposed conceptual model a further research process is proposed. The study additionally provides a number of new perspectives on the characteristics of service systems, and the nature of decision making and requirements for decision support in service systems that can potentially provide a basis for further discussion and research, and support the practice alike.

A mechanistic view of mitochondrial death decision pores

Mitochondria increase their outer and inner membrane permeability to solutes, protons and metabolites in response to a variety of extrinsic and intrinsic signaling events. The maintenance of cellular and intraorganelle ionic homeostasis, particularly for Ca2+, can determine cell survival or death. Mitochondrial death decision is centered on two processes: inner membrane permeabilization, such as that promoted by the mitochondrial permeability transition pore, formed across inner membranes when Ca2+ reaches a critical threshold, and mitochondrial outer membrane permeabilization, in which the pro-apoptotic proteins BID, BAX, and BAK play active roles. Membrane permeabilization leads to the release of apoptogenic proteins: cytochrome c, apoptosis-inducing factor, Smac/Diablo, HtrA2/Omi, and endonuclease G. Cytochrome c initiates the proteolytic activation of caspases, which in turn cleave hundreds of proteins to produce the morphological and biochemical changes of apoptosis. Voltage-dependent anion channel, cyclophilin D, adenine nucleotide translocase, and the pro-apoptotic proteins BID, BAX, and BAK may be part of the molecular composition of membrane pores leading to mitochondrial permeabilization, but this remains a central question to be resolved. Other transporting pores and channels, including the ceramide channel, the mitochondrial apoptosis-induced channel, as well as a non-specific outer membrane rupture may also be potential release pathways for these apoptogenic factors. In this review, we discuss the mechanistic models by which reactive oxygen species and caspases, via structural and conformational changes of membrane lipids and proteins, promote conditions for inner/outer membrane permeabilization, which may be followed by either opening of pores or a rupture of the outer mitochondrial membrane.

Can future systemic financial risks be quantified?: ergodic vs nonergodic stochastic processes

Different axioms underlie efficient market theory and Keynes's liquidity preference theory. Efficient market theory assumes the ergodic axiom. Consequently, today's decision makers can calculate with actuarial precision the future value of all possible outcomes resulting from today's decisions. Since in an efficient market world decision makers "know" their intertemporal budget constraints, decision makers never default on a loan, i.e., systemic defaults, insolvencies, and bankruptcies are impossible. Keynes liquidity preference theory rejects the ergodic axiom. The future is ontologically uncertain. Accordingly systemic defaults and insolvencies can occur but can never be predicted in advance.

Interactions of decision mechanisms in an entrepreneurial firm’s journey to become a major player in its industry

This study discusses the interactions of different decision-making mechanisms in the process of change of a successful entrepreneurial dairy firm in Vietnam. The purpose of the study is to construct a theoretical framework, which explains the interactions between effectual and causal decision-making processes in different phases of business, and to provide a real life example with practical recommendations for entrepreneurs and managers. In order to achieve this purpose, a preliminary theoretical framework was built, using process theories applied to different decision making modes, referred to as causation and effectuation. The case was studied through ethnographic research method, with three semi-structured interviews, one unstructured interview, secondary data and observations within four months in 2013-2014. After the data was analyzed, a modified framework was drawn from the result. The finding of this study shows that there was an interaction between effectual and causal decision-making processes in different stages of the company’s development. The entrepreneur applied effectual decision-making process to develop a unique business model and a new dairy market segment. However, when a new market demand arose, the company’s resources became insufficient, they thus had to shift to causation process to adapt to market change. Simultaneously, with better-accumulated resources, the entrepreneur continued the effectuation process to create another brand new dairy market segment. This study, thus, contributes to effectuation theory, emphasizing the necessity of combining effectual and causal decision-making processes in different phases of business. It is suggested that business would develop with an effectual process until a business model is viable for growth. It continues to use this process up to a certain degree. When the market changes, the company needs to collect more means to adapt to the changes. They need to set new goals and this is a shift to the use of causal process, which builds on prediction. It uses goals and teleology as driving mechanisms and tries to exploit and fill potential resource gaps to achieve these goals. At the same time, there are new iterations that look to establish new lines or types of business with the given means, which are now well established. This again employs effectual mechanisms, which are based on evolutionary process, until they reach the stage of viable tested business model. Moreover, this study hopes to provide know-how to entrepreneurs and managers of small companies in similar situations, suggesting how to combine effectual and causal decision-making processes to deal with various circumstances in different times.

Markovian Processes, Two-Sided Autoregressions and Finite-Sample Inference for Stationary and Nonstationary Autoregressive Processes.

In this paper, we develop finite-sample inference procedures for stationary and nonstationary autoregressive (AR) models. The method is based on special properties of Markov processes and a split-sample technique. The results on Markovian processes (intercalary independence and truncation) only require the existence of conditional densities. They are proved for possibly nonstationary and/or non-Gaussian multivariate Markov processes. In the context of a linear regression model with AR(1) errors, we show how these results can be used to simplify the distributional properties of the model by conditioning a subset of the data on the remaining observations. This transformation leads to a new model which has the form of a two-sided autoregression to which standard classical linear regression inference techniques can be applied. We show how to derive tests and confidence sets for the mean and/or autoregressive parameters of the model. We also develop a test on the order of an autoregression. We show that a combination of subsample-based inferences can improve the performance of the procedure. An application to U.S. domestic investment data illustrates the method.

Problèmes de premier passage et de commande optimale pour des chaînes de Markov à temps discret.

Nous considérons des processus de diffusion, définis par des équations différentielles stochastiques, et puis nous nous intéressons à des problèmes de premier passage pour les chaînes de Markov en temps discret correspon- dant à ces processus de diffusion. Comme il est connu dans la littérature, ces chaînes convergent en loi vers la solution des équations différentielles stochas- tiques considérées. Notre contribution consiste à trouver des formules expli- cites pour la probabilité de premier passage et la durée de la partie pour ces chaînes de Markov à temps discret. Nous montrons aussi que les résultats ob- tenus convergent selon la métrique euclidienne (i.e topologie euclidienne) vers les quantités correspondantes pour les processus de diffusion. En dernier lieu, nous étudions un problème de commande optimale pour des chaînes de Markov en temps discret. L’objectif est de trouver la valeur qui mi- nimise l’espérance mathématique d’une certaine fonction de coût. Contraire- ment au cas continu, il n’existe pas de formule explicite pour cette valeur op- timale dans le cas discret. Ainsi, nous avons étudié dans cette thèse quelques cas particuliers pour lesquels nous avons trouvé cette valeur optimale.

Bioethics in Health Policy Development: A Primer for Decision-Makers

Article

Parameter Estimation in Minification Processes

In this article it is proved that the stationary Markov sequences generated by minification models are ergodic and uniformly mixing. These results are used to establish the optimal properties of estimators for the parameters in the model. The problem of estimating the parameters in the exponential minification model is discussed in detail.

Development of Best Practices for Ship Recycling Processes

Ship recycling has been considered as the best means to dispose off an obsolete ship. The current state of art of technology combined with the demands of sustainable developments from the global maritime industrial sector has modified the status of erstwhile ‘ship breaking’ involving ship scrap business to a modern industry undertaking dismantling of ships and recycling/reusing the dismantled products in a supply chain of pre owned product market by following the principles of recycling. Industries will have to formulate a set of best practices and blend them with the engineering activities for producing better quality products, improving the productivity and for achieving improved performances related to sustainable development. Improved performance by industries in a sustainable development perspective is accomplished only by implementing the 4E principles, ie.,. ecofriendliness, engineering efficiency, energy conservation and ergonomics in their core operations. The present study has done a comprehensive investigation into various ship recycling operations for formulating a set of best practices.Being the ultimate life cycle stage of a ship, ship recycling activities incorporate certain commercial procedures well in advance to facilitate the objectives of dismantling and recycling/reusing of various parts of the vessel. Thorough knowledge regarding these background procedures in ship recycling is essential for examining and understanding the industrial business operations associated with it. As a first step, the practices followed in merchant shipping operations regarding the decision on decommissioning have been and made available in the thesis. Brief description about the positioning methods and important preparations for the most feasible ship recycling method ie.,. beach method have been provided as a part of the outline of the background information. Available sources of guidelines, codes and rules & regulations for ship recycling have been compiled and included in the discussion.Very brief summary of practices in major ship recycling destinations has been prepared and listed for providing an overview of the global ship recycling activities. The present status of ship recycling by treating it as a full fledged engineering industry has been brought out to establish the need for looking into the development of the best practices. Major engineering attributes of ship as a unique engineering product and the significant influencing factors on her life cycle stage operations have been studied and added to the information base on ship recycling. Role of ship recycling industry as an important player in global sustainable development efforts has been reviewed by analysing the benefits of ship recycling. A brief synopsis on the state of art of ship recycling in major international ship recycling centres has also been incorporated in the backdrop knowledgebase generation on ship recycling processes.Publications available in this field have been reviewed and classified into five subject categories viz., Infrastructure for recycling yards and methods of dismantling, Rules regarding ship recycling activities, Environmental and safety aspects of ship recycling, Role of naval architects and ship classification societies, Application of information technology and Demand forecasting. The inference from the literature survey have been summarised and recorded. Noticeable observations in the inference include need of creation of a comprehensive knowledgebase on ship recycling and its effective implementation in the industry and the insignificant involvement of naval architects and shipbuilding engineers in ship recycling industry. These two important inferences and the message conveyed by them have been addressed with due importance in the subsequent part of the present study.As a part of the study the importance of demand forecasting in ship recycling has been introduced and presented. A sample input for ship recycling data for implementation of computer based methods of demand forecasting has been presented in this section of the thesis.The interdisciplinary nature of engineering processes involved in ship recycling has been identified as one of the important features of this industry. The present study has identified more than a dozen major stake holders in ship recycling having their own interests and roles. It has also been observed that most of the ship recycling activities is carried out in South East Asian countries where the beach based ship recycling is done in yards without proper infrastructure support. A model of beach based ship recycling has been developed and the roles, responsibilities and the mutual interactions of the elements of the system have been documented as a part of the study Subsequently the need of a generation of a wide knowledgebase on ship recycling activities as pointed out by the literature survey has been addressed. The information base and source of expertise required to build a broad knowledgebase on ship recycling operations have been identified and tabulated. Eleven important ship recycling processes have been identified and a brief sketch of steps involved in these processes have been examined and addressed in detail. Based on these findings, a detailed sequential disassembly process plan of ship recycling has been prepared and charted. After having established the need of best practices in ship recycling initially, the present study here identifies development of a user friendly expert system for ship recycling process as one of the constituents of the proposed best practises. A user friendly expert system has been developed for beach based ship recycling processes and is named as Ship Recycling Recommender (SRR). Two important functions of SRR, first one for the ‘Administrators’, the stake holders at the helm of the ship recycling affairs and second one for the ‘Users’, the stake holders who execute the actual dismantling have been presented by highlighting the steps involved in the execution of the software. The important output generated, ie.,. recommended practices for ship dismantling processes and safe handling information on materials present onboard have been presented with the help of ship recycling reports generated by the expert system. A brief account of necessity of having a ship recycling work content estimation as part of the best practices has been presented in the study. This is supported by a detailed work estimation schedule for the same as one of the appendices.As mentioned earlier, a definite lack of involvement of naval architect has been observed in development of methodologies for improving the status of ship recycling industry. Present study has put forward a holistic approach to review the status of ship recycling not simply as end of life activity of all ‘time expired’ vessels, but as a focal point of integrating all life cycle activities. A new engineering design philosophy targeting sustainable development of marine industrial domain, named design for ship recycling has been identified, formulated and presented. A new model of ship life cycle has been proposed by adding few stages to the traditional life cycle after analysing their critical role in accomplishing clean and safe end of life and partial dismantling of ships. Two applications of design for ship recycling viz, recyclability of ships and her products and allotment of Green Safety Index for ships have been presented as a part of implementation of the philosophy in actual practice.

Decision‐Making in Public Good Dilemmas: Theory and Agent‐Based Simulation

In many real world contexts individuals find themselves in situations where they have to decide between options of behaviour that serve a collective purpose or behaviours which satisfy one’s private interests, ignoring the collective. In some cases the underlying social dilemma (Dawes, 1980) is solved and we observe collective action (Olson, 1965). In others social mobilisation is unsuccessful. The central topic of social dilemma research is the identification and understanding of mechanisms which yield to the observed cooperation and therefore resolve the social dilemma. It is the purpose of this thesis to contribute this research field for the case of public good dilemmas. To do so, existing work that is relevant to this problem domain is reviewed and a set of mandatory requirements is derived which guide theory and method development of the thesis. In particular, the thesis focusses on dynamic processes of social mobilisation which can foster or inhibit collective action. The basic understanding is that success or failure of the required process of social mobilisation is determined by heterogeneous individual preferences of the members of a providing group, the social structure in which the acting individuals are contained, and the embedding of the individuals in economic, political, biophysical, or other external contexts. To account for these aspects and for the involved dynamics the methodical approach of the thesis is computer simulation, in particular agent-based modelling and simulation of social systems. Particularly conductive are agent models which ground the simulation of human behaviour in suitable psychological theories of action. The thesis develops the action theory HAPPenInGS (Heterogeneous Agents Providing Public Goods) and demonstrates its embedding into different agent-based simulations. The thesis substantiates the particular added value of the methodical approach: Starting out from a theory of individual behaviour, in simulations the emergence of collective patterns of behaviour becomes observable. In addition, the underlying collective dynamics may be scrutinised and assessed by scenario analysis. The results of such experiments reveal insights on processes of social mobilisation which go beyond classical empirical approaches and yield policy recommendations on promising intervention measures in particular.