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Code IF 467
Term 201702
Title Decision, Psychology and Brain
Faculty Faculty of Engineering and Natural Sciences
Subject Interfaculty Course(IF)
SU Credit 3
ECTS Credit 6.00 / 6.00 ECTS (for students admitted in the 2013-14 Academic Year or following years)
Instructor(s) Kemal K?l?c kkilic@sabanciuniv.edu,
Detailed Syllabus
Language of Instruction English
Level of Course Undergraduate
Type of Course Click here to view.
Prerequisites
(only for SU students)
MATH203 NS201
Mode of Delivery Formal lecture,Interactive lecture
Planned Learning Activities Interactive,Communicative,Discussion based learning
Content

Many scientific fields such as neuroscience, psychology, operations research, management science have modeled, analyzed, and tried to understand how people make decisions, with various tools, techniques and approaches within their own conventional theoretical frameworks. Recent advances in technology have accelerated brain research, and have given the opportunity to experiment and question the theoretical frameworks related to decision making developed in various disciplines. In this regard, decision making has become of particular interest to scientific fields such as cognitive and behavioral neuroscience, cognitive psychology, computational sociology and neuroeconomics. In this course, the students will learn how to model realistically and consistently the basic elements of decision making, i.e., the value system and objectives , alternatives, uncertainties, and preferences, based on the mathematical frameworks provided by various fields such as economics, operations research, computer sciences, as well as cognitive, physiological, and behavioral neuroscience. In the course, some mathematical tools, techniques and approaches (e.g., decision trees, game theory, mathematical programming, modeling uncertainty and Bayes theorem, Bayesian learning, modeling of preferences and vNM utility theory, entropy, decision tree learning and artificial neural networks) which will provide an analytical framework for decision making an learning will be covered. Aside to these techniques findings from the recently growing fields such as neuroeconomics, behavioral economics and behavioral neuroscience (e.g., prospect theory, conditioning, reinforcement, reward and punishment, expectation of judgment and decision- making, experience and deferral) will also be discussed within the same framework. In the course neural processes and mechanisms of social and individual decision making, behavior and choice (e.g., reward perception, learning types, attention, memory, belief systems, interaction with motor processes, trust, cooperation, alturism, social behavior) will be addressed and supported by neuroethological comparisons.

Objective

In this course, the students will learn how to model realistically and consistently the basic elements of decision making, i.e., the value system and objectives, alternatives, uncertainties, and preferences, based on the mathematical frameworks provided by various fields such as economics, operations research, computer sciences, as well as cognitive, physiological, and behavioral neuroscience.

Learning Outcome

Discuss the role of the various neurochemicals in reward processing and the physiology of the sensory and motor processes.
Understand the neuroscience of choice and related behaviors, social and individual decision making in the context of human and comparative (other animals) neural systems.
Model decision making problems with analytical tools such as decision trees, game theory, and mathematical programming.
Model uncertainty, discuss biases that uncertainty might cause in decision making and learn the current evidence from neuroscience regarding to the mechanisms that brain handles uncertainty.
Develop basic understanding of machine learning tools such as decision trees, Bayesian learning, reinforcement learning, artificial neural networks, and their possible contribution to understanding the biological mechanisms that takes place in brain.
Understand the prospect theory, heuristics, biases, and their neural bases.
Understand the formal theoretical framework of utility theory and discuss the problems associated with it in learning and decision making processes.

Programme Outcomes
 
Common Outcomes For All Programs
1 Understand the world, their country, their society, as well as themselves and have awareness of ethical problems, social rights, values and responsibility to the self and to others. 3
2 Understand different disciplines from natural and social sciences to mathematics and art, and develop interdisciplinary approaches in thinking and practice. 5
3 Think critically, follow innovations and developments in science and technology, demonstrate personal and organizational entrepreneurship and engage in life-long learning in various subjects. 4
4 Communicate effectively in Turkish and English by oral, written, graphical and technological means. 4
5 Take individual and team responsibility, function effectively and respectively as an individual and a member or a leader of a team; and have the skills to work effectively in multi-disciplinary teams. 3
Common Outcomes ForFaculty of Eng. & Natural Sci.
1 Possess sufficient knowledge of mathematics, science and program-specific engineering topics; use theoretical and applied knowledge of these areas in complex engineering problems. 5
2 Identify, define, formulate and solve complex engineering problems; choose and apply suitable analysis and modeling methods for this purpose. 5
3 Develop, choose and use modern techniques and tools that are needed for analysis and solution of complex problems faced in engineering applications; possess knowledge of standards used in engineering applications; use information technologies effectively. 2
4 Ability to design a complex system, process, instrument or a product under realistic constraints and conditions, with the goal of fulfilling specified needs; apply modern design techniques for this purpose. 2
5 Design and conduct experiments, collect data, analyze and interpret the results to investigate complex engineering problems or program-specific research areas. 2
6 Knowledge of business practices such as project management, risk management and change management; awareness on innovation; knowledge of sustainable development. 1
7 Knowledge of impact of engineering solutions in a global, economic, environmental, health and societal context; knowledge of contemporary issues; awareness on legal outcomes of engineering solutions; understanding of professional and ethical responsibility. 4
1 Develop a thorough knowledge of theories, concepts, and research methods in the field and apply them in research design and data analysis.
2 Assess the impact of the economic, social, and political environment from a global, national and regional level.
3 Know how to access written and visual, primary and secondary sources of information, interpret concepts and data from a variety of sources in developing disciplinary and interdisciplinary analyses.
Common Outcomes ForSchool of Management
1 Demonstrate an understanding of economics, and main functional areas of management. 4
2 Assess the impact of the economic, social, and political environment from a global, national and regional level. 2
Materials Science and Nano Engineering Program Outcomes Area Electives
1 Applying fundamental and advanced knowledge of natural sciences as well as engineering principles to develop and design new materials and establish the relation between internal structure and physical properties using experimental, computational and theoretical tools. 4
2 Merging the existing knowledge on physical properties, design limits and fabrication methods in materials selection for a particular application or to resolve material performance related problems. 3
3 Predicting and understanding the behavior of a material under use in a specific environment knowing the internal structure or vice versa. 5
Computer Science and Engineering Program Outcomes Area Electives
1 Design, implement, test, and evaluate a computer system, component, or algorithm to meet desired needs and to solve a computational problem. 4
2 Demonstrate knowledge of discrete mathematics and data structures. 3
3 Demonstrate knowledge of probability and statistics, including applications appropriate to computer science and engineering. 5
Industrial Engineering Program Outcomes Area Electives
1 Formulate and analyze problems in complex manufacturing and service systems by comprehending and applying the basic tools of industrial engineering such as modeling and optimization, stochastics, statistics. 5
2 Design and develop appropriate analytical solution strategies for problems in integrated production and service systems involving human capital, materials, information, equipment, and energy. 2
3 Implement solution strategies on a computer platform for decision-support purposes by employing effective computational and experimental tools. 4
Management Program Outcomes Area Electives
1 Pursue open minded inquiry and appreciate the importance of research as an input into management practice. 4
2 Know how to access, interpret and analyze data and information and use them to make informed decisions. 5
3 Work effectively in environments characterized by people of diverse educational, social and cultural backgrounds. 3
4 Identify, select, and justify strategies and courses of action at the divisional, business, and corporate levels of analysis and to develop effective plans for the implementation of selected strategies. 2
Assessment Methods and Criteria
  Percentage (%)
Final 34
Exam 66