Info
- Utilizzare la checkbox di selezione a fianco di ciascun documento per attivare le funzionalità di stampa, invio email, download nei formati disponibili del (i) record.
Decision making under uncertainty : theory and application / / Mykel J. Kochenderfer, with contributions from Christopher Amato, Girish Chowdhary, Jonathan P. How, Hayley J. Davison Reynolds, Jason R. Thornton, Pedro A. Torres-Carrasquillo, N. Kemal Üre, John Vian
| Decision making under uncertainty : theory and application / / Mykel J. Kochenderfer, with contributions from Christopher Amato, Girish Chowdhary, Jonathan P. How, Hayley J. Davison Reynolds, Jason R. Thornton, Pedro A. Torres-Carrasquillo, N. Kemal Üre, John Vian |
| Autore | Kochenderfer Mykel J. <1980-> |
| Pubbl/distr/stampa | Cambridge, Massachusetts : , : MIT Press, , [2015] |
| Descrizione fisica | 1 PDF (xxv, 323 pages) : illustrations (some color), portraits |
| Disciplina | 003/.56 |
| Collana | Lincoln Laboratory series |
| Soggetto topico |
Intelligent control systems
Automatic machinery Decision making - Mathematical models |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910260595903321 |
Kochenderfer Mykel J. <1980->
|
||
| Cambridge, Massachusetts : , : MIT Press, , [2015] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Decision making under uncertainty and constraints : a why-book / / edited by Martine Ceberio and Vladik Kreinovich
| Decision making under uncertainty and constraints : a why-book / / edited by Martine Ceberio and Vladik Kreinovich |
| Edizione | [1st ed. 2023.] |
| Pubbl/distr/stampa | Cham, Switzerland : , : Springer, , [2023] |
| Descrizione fisica | 1 online resource (x, 304 pages) : illustrations (some color) |
| Disciplina | 327.120971 |
| Collana | Studies in Systems, Decision and Control |
| Soggetto topico |
Uncertainty
Decision making - Mathematical models |
| ISBN | 3-031-16415-6 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Baudelaire's Ideas of Vagueness and Uniqueness in Art: Algorithm-Based Explanations -- Selfish Gene Theory Explains Oedipus Complex -- How to Teach Advanced Highly Motivated Students: Teaching Strategy of Iosif Yakovlevich Verebeichik -- Why 70/100 Is Satisfactory? Why Five Letter Grades? Why Other Academic Conventions? -- Shall We Ignore All Intermediate Grades? -- Why ∞ is a Reasonable Symbol for Infinity -- What is 1/0 from the Practical Viewpoint: A Pedagogical Note -- Historical Diversity Through base-10 Representation of Mayan Maths. |
| Record Nr. | UNINA-9910640379403321 |
| Cham, Switzerland : , : Springer, , [2023] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Decision making under uncertainty and reinforcement learning : theory and algorithms / / Christos Dimitrakakis, Ronald Ortner
| Decision making under uncertainty and reinforcement learning : theory and algorithms / / Christos Dimitrakakis, Ronald Ortner |
| Autore | Dimitrakakis Christos |
| Pubbl/distr/stampa | Cham, Switzerland : , : Springer, , [2022] |
| Descrizione fisica | 1 online resource (251 pages) |
| Disciplina | 658.403 |
| Collana | Intelligent systems reference library |
| Soggetto topico |
Decision making - Mathematical models
Reinforcement learning Uncertainty |
| ISBN | 3-031-07614-1 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Intro -- Preface -- Acknowledgements -- Reference -- Contents -- 1 Introduction -- 1.1 Uncertainty and Probability -- 1.2 The Exploration-Exploitation Trade-Off -- 1.3 Decision Theory and Reinforcement Learning -- References -- 2 Subjective Probability and Utility -- 2.1 Subjective Probability -- 2.1.1 Relative Likelihood -- 2.1.2 Subjective Probability Assumptions -- 2.1.3 Assigning Unique Probabilities* -- 2.1.4 Conditional Likelihoods -- 2.1.5 Probability Elicitation -- 2.2 Updating Beliefs: Bayes' Theorem -- 2.3 Utility Theory -- 2.3.1 Rewards and Preferences -- 2.3.2 Preferences Among Distributions -- 2.3.3 Utility -- 2.3.4 Measuring Utility* -- 2.3.5 Convex and Concave Utility Functions -- 2.4 Exercises -- Reference -- 3 Decision Problems -- 3.1 Introduction -- 3.2 Rewards that Depend on the Outcome of an Experiment -- 3.2.1 Formalisation of the Problem Setting -- 3.2.2 Decision Diagrams -- 3.2.3 Statistical Estimation* -- 3.3 Bayes Decisions -- 3.3.1 Convexity of the Bayes-Optimal Utility* -- 3.4 Statistical and Strategic Decision Making -- 3.4.1 Alternative Notions of Optimality -- 3.4.2 Solving Minimax Problems* -- 3.4.3 Two-Player Games -- 3.5 Decision Problems with Observations -- 3.5.1 Maximizing Utility When Making Observations -- 3.5.2 Bayes Decision Rules -- 3.5.3 Decision Problems in Classification -- 3.5.4 Calculating Posteriors -- 3.6 Summary -- 3.7 Exercises -- 3.7.1 Problems with No Observations -- 3.7.2 Problems with Observations -- 3.7.3 An Insurance Problem -- 3.7.4 Medical Diagnosis -- References -- 4 Estimation -- 4.1 Introduction -- 4.2 Sufficient Statistics -- 4.2.1 Sufficient Statistics -- 4.2.2 Exponential Families -- 4.3 Conjugate Priors -- 4.3.1 Bernoulli-Beta Conjugate Pair -- 4.3.2 Conjugates for the Normal Distribution -- 4.3.3 Conjugates for Multivariate Distributions -- 4.4 Credible Intervals.
4.5 Concentration Inequalities -- 4.5.1 Chernoff-Hoeffding Bounds -- 4.6 Approximate Bayesian Approaches -- 4.6.1 Monte Carlo Inference -- 4.6.2 Approximate Bayesian Computation -- 4.6.3 Analytic Approximations of the Posterior -- 4.6.4 Maximum Likelihood and Empirical Bayes Methods -- References -- 5 Sequential Sampling -- 5.1 Gains From Sequential Sampling -- 5.1.1 An Example: Sampling with Costs -- 5.2 Optimal Sequential Sampling Procedures -- 5.2.1 Multi-stage Problems -- 5.2.2 Backwards Induction for Bounded Procedures -- 5.2.3 Unbounded Sequential Decision Procedures -- 5.2.4 The Sequential Probability Ratio Test -- 5.2.5 Wald's Theorem -- 5.3 Martingales -- 5.4 Markov Processes -- 5.5 Exercises -- 6 Experiment Design and Markov Decision Processes -- 6.1 Introduction -- 6.2 Bandit Problems -- 6.2.1 An Example: Bernoulli Bandits -- 6.2.2 Decision-Theoretic Bandit Process -- 6.3 Markov Decision Processes and Reinforcement Learning -- 6.3.1 Value Functions -- 6.4 Finite Horizon, Undiscounted Problems -- 6.4.1 Direct Policy Evaluation -- 6.4.2 Backwards Induction Policy Evaluation -- 6.4.3 Backwards Induction Policy Optimization -- 6.5 Infinite-Horizon -- 6.5.1 Examples -- 6.5.2 Markov Chain Theory for Discounted Problems -- 6.5.3 Optimality Equations -- 6.5.4 MDP Algorithms for Infinite Horizon and Discounted Rewards -- 6.6 Optimality Criteria -- 6.7 Summary -- 6.8 Further Reading -- 6.9 Exercises -- 6.9.1 MDP Theory -- 6.9.2 Automatic Algorithm Selection -- 6.9.3 Scheduling -- 6.9.4 General Questions -- References -- 7 Simulation-Based Algorithms -- 7.1 Introduction -- 7.1.1 The Robbins-Monro Approximation -- 7.1.2 The Theory of the Approximation -- 7.2 Dynamic Problems -- 7.2.1 Monte Carlo Policy Evaluation and Iteration -- 7.2.2 Monte Carlo Updates -- 7.2.3 Temporal Difference Methods -- 7.2.4 Stochastic Value Iteration Methods. 7.3 Discussion -- 7.4 Exercises -- References -- 8 Approximate Representations -- 8.1 Introduction -- 8.1.1 Fitting a Value Function -- 8.1.2 Fitting a Policy -- 8.1.3 Features -- 8.1.4 Estimation Building Blocks -- 8.1.5 The Value Estimation Step -- 8.1.6 Policy Estimation -- 8.2 Approximate Policy Iteration (API) -- 8.2.1 Error Bounds for Approximate Value Functions -- 8.2.2 Rollout-Based Policy Iteration Methods -- 8.2.3 Least Squares Methods -- 8.3 Approximate Value Iteration -- 8.3.1 Approximate Backwards Induction -- 8.3.2 State Aggregation -- 8.3.3 Representative State Approximation -- 8.3.4 Bellman Error Methods -- 8.4 Policy Gradient -- 8.4.1 Stochastic Policy Gradient -- 8.4.2 Practical Considerations -- 8.5 Examples -- 8.6 Further Reading -- 8.7 Exercises -- References -- 9 Bayesian Reinforcement Learning -- 9.1 Introduction -- 9.1.1 Acting in Unknown MDPs -- 9.1.2 Updating the Belief -- 9.2 Finding Bayes-Optimal Policies -- 9.2.1 The Expected MDP Heuristic -- 9.2.2 The Maximum MDP Heuristic -- 9.2.3 Bayesian Policy Gradient -- 9.2.4 The Belief-Augmented MDP -- 9.2.5 Branch and Bound -- 9.2.6 Bounds on the Expected Utility -- 9.2.7 Estimating Lower Bounds on the Value Function with Backwards Induction -- 9.2.8 Further Reading -- 9.3 Bayesian Methods in Continuous Spaces -- 9.3.1 Linear-Gaussian Transition Models -- 9.3.2 Approximate Dynamic Programming -- 9.4 Partially Observable Markov Decision Processes -- 9.4.1 Solving Known POMDPs -- 9.4.2 Solving Unknown POMDPs -- 9.5 Relations Between Different Settings -- 9.6 Exercises -- References -- 10 Distribution-Free Reinforcement Learning -- 10.1 Introduction -- 10.2 Finite Stochastic Bandit Problems -- 10.2.1 The UCB1 Algorithm -- 10.2.2 Non i.i.d. Rewards -- 10.3 Reinforcement Learning in MDPs -- 10.3.1 An Upper-Confidence Bound Algorithm -- 10.3.2 Bibliographical Remarks -- References. 11 Conclusion -- Appendix Symbols -- Appendix Index -- Index. |
| Record Nr. | UNINA-9910633910303321 |
|
Dimitrakakis Christos
|
||
| Cham, Switzerland : , : Springer, , [2022] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Decision-making process : Concepts and methods [[electronic resource] /] / edited by Denis Bouyssou ... [et al.]
| Decision-making process : Concepts and methods [[electronic resource] /] / edited by Denis Bouyssou ... [et al.] |
| Pubbl/distr/stampa | Hoboken, NJ, : John Wiley & Sons, 2009 |
| Descrizione fisica | 1 online resource (904 p.) |
| Disciplina |
658.4/03
658.403 |
| Altri autori (Persone) | BouyssouD (Denis) |
| Collana | ISTE |
| Soggetto topico |
Decision support systems
Decision making - Mathematical models |
| Soggetto genere / forma | Electronic books. |
| ISBN |
1-282-68400-0
9786612684005 0-470-61187-1 0-470-61030-1 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Decision-making Process; Contents; Preface; Chapter 1. From Decision Theory to Decision-aiding Methodology; 1.1. Introduction; 1.2. History; 1.2.1. Genesis and youth; 1.2.2. Maturity; 1.3. Different decision-aiding approaches; 1.4. The decision-aiding process; 1.4.1. The problem situation; 1.4.2. The problem formulation; 1.4.3. The evaluation model; 1.4.4. The final recommendation; 1.5. Conclusion; 1.6. Acknowledgements; 1.7. Bibliography; Chapter 2. Binary Relations and Preference Modeling; 2.1. Introduction; 2.2. Binary relations; 2.2.1. Definitions; 2.2.2. Properties of a binary relation
2.2.3. Graphical representation of a binary relation2.2.4. Matrix representation of a binary relation; 2.2.5. Example; 2.3. Binary relations and preference structures; 2.4. Classical preference structures; 2.4.1. Total order; 2.4.1.1. Definition; 2.4.1.2. Numerical representation; 2.4.2. Weak orders; 2.4.2.1. Definition; 2.4.2.2. Numerical representation; 2.4.3. Classical problems; 2.4.3.1. Choosing on the basis of binary relation; 2.4.3.2. Aggregating preferences; 2.4.3.3. Particular structure of the set of objects; 2.5. Semi-orders and interval orders; 2.5.1. Semi-order; 2.5.1.1. Definition 2.5.1.2. Weak order associated with a semi-order2.5.1.3. Matrix representation; 2.5.1.4. Numerical representation; 2.5.2. Interval order; 2.5.2.1. Definition; 2.5.2.2. Weak orders associated with an interval order; 2.5.2.3. Matrix representation; 2.5.2.4. Numerical representation; 2.5.3. Remarks; 2.6. Preference structures with incomparability; 2.6.1. Partial order; 2.6.2. Quasi-order; 2.6.3. Synthesis; 2.7. Conclusion; 2.7.1. Other preference structures; 2.7.2. Other problems; 2.8. Bibliography; Chapter 3. Formal Representations of Uncertainty; 3.1. Introduction 3.2. Information: a typology of defects3.2.1. Incompleteness and imprecision; 3.2.2. Uncertainty; 3.2.3. Gradual linguistic information; 3.2.4. Granularity; 3.3. Probability theory; 3.3.1. Frequentists and subjectivists; 3.3.2. Conditional probability; 3.3.3. The unique probability assumption in the subjective setting; 3.4. Incompleteness-tolerant numerical uncertainty theories; 3.4.1. Imprecise probabilities; 3.4.2. Random disjunctive sets and belief functions; 3.4.3. Quantitative possibility theory; 3.4.3.1. Possibility theory and belief functions 3.4.3.2. Possibility theory and imprecise probabilities3.4.3.3. Clouds and generalized p-boxes; 3.4.3.4. Possibility-probability transformations; 3.4.4. Possibility theory and non-Bayesian statistics; 3.5. Qualitative uncertainty representations; 3.6. Conditioning in non-additive representations; 3.6.1. Conditional events and qualitative conditioning; 3.6.2. Conditioning for belief functions and imprecise probabilities; 3.7. Fusion of imprecise and uncertain information; 3.7.1. Non-Bayesian probabilistic fusion; 3.7.2. Bayesian probabilistic fusion; 3.7.3. Fusion in possibility theory 3.7.4. Fusion of belief functions |
| Record Nr. | UNINA-9910139468803321 |
| Hoboken, NJ, : John Wiley & Sons, 2009 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Decision-making process : Concepts and methods [[electronic resource] /] / edited by Denis Bouyssou ... [et al.]
| Decision-making process : Concepts and methods [[electronic resource] /] / edited by Denis Bouyssou ... [et al.] |
| Pubbl/distr/stampa | Hoboken, NJ, : John Wiley & Sons, 2009 |
| Descrizione fisica | 1 online resource (904 p.) |
| Disciplina |
658.4/03
658.403 |
| Altri autori (Persone) | BouyssouD (Denis) |
| Collana | ISTE |
| Soggetto topico |
Decision support systems
Decision making - Mathematical models |
| ISBN |
1-282-68400-0
9786612684005 0-470-61187-1 0-470-61030-1 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Decision-making Process; Contents; Preface; Chapter 1. From Decision Theory to Decision-aiding Methodology; 1.1. Introduction; 1.2. History; 1.2.1. Genesis and youth; 1.2.2. Maturity; 1.3. Different decision-aiding approaches; 1.4. The decision-aiding process; 1.4.1. The problem situation; 1.4.2. The problem formulation; 1.4.3. The evaluation model; 1.4.4. The final recommendation; 1.5. Conclusion; 1.6. Acknowledgements; 1.7. Bibliography; Chapter 2. Binary Relations and Preference Modeling; 2.1. Introduction; 2.2. Binary relations; 2.2.1. Definitions; 2.2.2. Properties of a binary relation
2.2.3. Graphical representation of a binary relation2.2.4. Matrix representation of a binary relation; 2.2.5. Example; 2.3. Binary relations and preference structures; 2.4. Classical preference structures; 2.4.1. Total order; 2.4.1.1. Definition; 2.4.1.2. Numerical representation; 2.4.2. Weak orders; 2.4.2.1. Definition; 2.4.2.2. Numerical representation; 2.4.3. Classical problems; 2.4.3.1. Choosing on the basis of binary relation; 2.4.3.2. Aggregating preferences; 2.4.3.3. Particular structure of the set of objects; 2.5. Semi-orders and interval orders; 2.5.1. Semi-order; 2.5.1.1. Definition 2.5.1.2. Weak order associated with a semi-order2.5.1.3. Matrix representation; 2.5.1.4. Numerical representation; 2.5.2. Interval order; 2.5.2.1. Definition; 2.5.2.2. Weak orders associated with an interval order; 2.5.2.3. Matrix representation; 2.5.2.4. Numerical representation; 2.5.3. Remarks; 2.6. Preference structures with incomparability; 2.6.1. Partial order; 2.6.2. Quasi-order; 2.6.3. Synthesis; 2.7. Conclusion; 2.7.1. Other preference structures; 2.7.2. Other problems; 2.8. Bibliography; Chapter 3. Formal Representations of Uncertainty; 3.1. Introduction 3.2. Information: a typology of defects3.2.1. Incompleteness and imprecision; 3.2.2. Uncertainty; 3.2.3. Gradual linguistic information; 3.2.4. Granularity; 3.3. Probability theory; 3.3.1. Frequentists and subjectivists; 3.3.2. Conditional probability; 3.3.3. The unique probability assumption in the subjective setting; 3.4. Incompleteness-tolerant numerical uncertainty theories; 3.4.1. Imprecise probabilities; 3.4.2. Random disjunctive sets and belief functions; 3.4.3. Quantitative possibility theory; 3.4.3.1. Possibility theory and belief functions 3.4.3.2. Possibility theory and imprecise probabilities3.4.3.3. Clouds and generalized p-boxes; 3.4.3.4. Possibility-probability transformations; 3.4.4. Possibility theory and non-Bayesian statistics; 3.5. Qualitative uncertainty representations; 3.6. Conditioning in non-additive representations; 3.6.1. Conditional events and qualitative conditioning; 3.6.2. Conditioning for belief functions and imprecise probabilities; 3.7. Fusion of imprecise and uncertain information; 3.7.1. Non-Bayesian probabilistic fusion; 3.7.2. Bayesian probabilistic fusion; 3.7.3. Fusion in possibility theory 3.7.4. Fusion of belief functions |
| Record Nr. | UNINA-9910830096503321 |
| Hoboken, NJ, : John Wiley & Sons, 2009 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Domain conditions in social choice theory / / Wulf Gaertner [[electronic resource]]
| Domain conditions in social choice theory / / Wulf Gaertner [[electronic resource]] |
| Autore | Gaertner Wulf |
| Pubbl/distr/stampa | Cambridge : , : Cambridge University Press, , 2001 |
| Descrizione fisica | 1 online resource (ix, 153 pages) : digital, PDF file(s) |
| Disciplina | 302/.13 |
| Soggetto topico |
Social choice - Mathematical models
Decision making - Mathematical models |
| ISBN |
1-107-12141-8
0-511-32817-6 0-511-15400-3 0-511-11898-8 0-521-79102-2 1-280-15929-4 0-511-49230-8 0-511-04671-5 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | ; 1. Introduction -- ; 2. Notation, definitions, and two fundamental theorems -- ; 3. The existence of collective choice rules under exclusion conditions for finite sets of discrete alternatives -- ; 4. Arrovian social welfare functions, nonmanipulable voting procedures and stable group decision functions -- ; 5. Restrictions on the distribution of individuals' preferences -- ; 6. The existence of social choice rules in n-dimensional continuous space -- ; 7. Concluding remarks. |
| Record Nr. | UNINA-9910455064703321 |
|
Gaertner Wulf
|
||
| Cambridge : , : Cambridge University Press, , 2001 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Domain conditions in social choice theory / / Wulf Gaertner [[electronic resource]]
| Domain conditions in social choice theory / / Wulf Gaertner [[electronic resource]] |
| Autore | Gaertner Wulf |
| Pubbl/distr/stampa | Cambridge : , : Cambridge University Press, , 2001 |
| Descrizione fisica | 1 online resource (ix, 153 pages) : digital, PDF file(s) |
| Disciplina | 302/.13 |
| Soggetto topico |
Social choice - Mathematical models
Decision making - Mathematical models |
| ISBN |
1-107-12141-8
0-511-32817-6 0-511-15400-3 0-511-11898-8 0-521-79102-2 1-280-15929-4 0-511-49230-8 0-511-04671-5 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | ; 1. Introduction -- ; 2. Notation, definitions, and two fundamental theorems -- ; 3. The existence of collective choice rules under exclusion conditions for finite sets of discrete alternatives -- ; 4. Arrovian social welfare functions, nonmanipulable voting procedures and stable group decision functions -- ; 5. Restrictions on the distribution of individuals' preferences -- ; 6. The existence of social choice rules in n-dimensional continuous space -- ; 7. Concluding remarks. |
| Record Nr. | UNINA-9910779923203321 |
|
Gaertner Wulf
|
||
| Cambridge : , : Cambridge University Press, , 2001 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Domain conditions in social choice theory / / Wulf Gaertner [[electronic resource]]
| Domain conditions in social choice theory / / Wulf Gaertner [[electronic resource]] |
| Autore | Gaertner Wulf |
| Pubbl/distr/stampa | Cambridge : , : Cambridge University Press, , 2001 |
| Descrizione fisica | 1 online resource (ix, 153 pages) : digital, PDF file(s) |
| Disciplina | 302/.13 |
| Soggetto topico |
Social choice - Mathematical models
Decision making - Mathematical models |
| ISBN |
1-107-12141-8
0-511-32817-6 0-511-15400-3 0-511-11898-8 0-521-79102-2 1-280-15929-4 0-511-49230-8 0-511-04671-5 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | ; 1. Introduction -- ; 2. Notation, definitions, and two fundamental theorems -- ; 3. The existence of collective choice rules under exclusion conditions for finite sets of discrete alternatives -- ; 4. Arrovian social welfare functions, nonmanipulable voting procedures and stable group decision functions -- ; 5. Restrictions on the distribution of individuals' preferences -- ; 6. The existence of social choice rules in n-dimensional continuous space -- ; 7. Concluding remarks. |
| Record Nr. | UNINA-9910820831803321 |
|
Gaertner Wulf
|
||
| Cambridge : , : Cambridge University Press, , 2001 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Economic decision analysis : for project feasibility studies / / Babak Jafarizadeh
| Economic decision analysis : for project feasibility studies / / Babak Jafarizadeh |
| Autore | Jafarizadeh Babak |
| Pubbl/distr/stampa | Cham, Switzerland : , : Springer, , [2022] |
| Descrizione fisica | 1 online resource (113 pages) |
| Disciplina | 658.15 |
| Collana | SpringerBriefs in Petroleum Geoscience and Engineering |
| Soggetto topico |
Corporations - Finance
Decision making - Mathematical models Feasibility studies |
| ISBN |
9783030961374
9783030961367 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910552718703321 |
|
Jafarizadeh Babak
|
||
| Cham, Switzerland : , : Springer, , [2022] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
An evolutionary model of industry transformation and the political sustainability of emission control policies / / Steven C. Isley [and three others]
| An evolutionary model of industry transformation and the political sustainability of emission control policies / / Steven C. Isley [and three others] |
| Autore | Isley Steven C. |
| Pubbl/distr/stampa | Santa Monica, California : , : RAND Corporation, , 2013 |
| Descrizione fisica | 1 online resource (104 pages) |
| Disciplina | 628.532 |
| Soggetto topico |
Carbon dioxide mitigation
Decision making - Mathematical models |
| ISBN | 0-8330-8308-2 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910220154303321 |
|
Isley Steven C.
|
||
| Santa Monica, California : , : RAND Corporation, , 2013 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
