Università degli studi dell'Insubria

TOPICS IN ECONOMICS I

Degree course: 
Corso di Second cycle degree in GLOBAL ENTREPRENEURSHIP ECONOMICS AND MANAGEMENT
Academyc year when starting the degree: 
2018/2019
Year: 
1
Academyc year when helding the course: 
2018/2019
Course type: 
Optional subjects
Language: 
English
Credits: 
3
Period: 
Second Trimestre
Standard lectures hours: 
30
Detail of lecture’s hours: 
Lesson (30 hours)
Final Examination: 
Orale

In this course, you are assessed by a take home exam at the end of the course. This consists of an essay that should be no longer than 5,000 words, excluding figures, tables, references, and appendices. The paper should introduce a model—the same you have produced in class during the semester. In so doing, you will:
(a) introduce the problem/topic and explain why it should be studied in general and why should it be studied with computational modeling;
(b) review some of the theory behind the choice;
(c) in connection with the theory, define the assumptions of the model;
(d) describe the components of the model and its procedures;
(e) explain how findings are produced and present them;
(f) comment on implications and write a short conclusion.
Further details on the exam will be disclosed in class as we move forward in the semester.

Assessment: 
Voto Finale

Computational simulations are not new to the social sciences. More or less sophisticated versions of them have been used in various disciplines over the decades. However, those disciplines that lean on modeling for conceptualizations and theory have mostly made use of equation-based and statistical approaches. Some of these technical solutions work well but become increasingly inadequate as the model approaches higher levels of complexity.
In spite of the difficulties, alternatives have been scarcely considered in the past because of two associated costs. On the one hand, computational capabilities have been limited and complex systems need large power in order to be performed appropriately. On the other hand, there are costs associated with learning a different technique, most likely a language (or code) for programming.
In the recent past, agent-based simulation emerged as a viable alternative specifically because the two costs mentioned above became less of a burden. Computational capabilities of the average computer machine allow for complex calculations and software allows programming to be less of a problem for researchers. This course takes advantage of these aspects and introduces ABM for the study of the economics of organizations.
The course starts with an introduction of modeling elements as well as some historical aspects that are deemed relevant to understand why ABM are structured the way they are. Basic concepts in modeling are covered but the idea is that of opening a door onto the world of computational modeling. For this reason, a few more advanced examples will be presented towards the end of the semester. The timetable is split into two parts for most of the semester: one theory lecture is usually followed by an applied session. The idea is to introduce concepts that can be then applied to actual modeling efforts. By the end of the semester, the student will have produced a model on a topic of interest.
Intended Learning Outcomes
By the end of the course, students will be able to
(a) understand strengths and weaknesses of ABM,
(b) assess when an agent-based approach should be used,
(c) critically evaluate some of the literature on computational simulation,
(d) learn basic programming skills (mostly on NetLogo),
(e) create simulation models.

ASSESSMENT: THE EXAM
In this course, you are assessed by a take home exam at the end of the course. This consists of an essay that should be no longer than 5,000 words, excluding figures, tables, references, and appendices. The paper should introduce a model—the same you have produced in class during the semester. In so doing, you will:
(a) introduce the problem/topic and explain why it should be studied in general and why should it be studied with computational modeling;
(b) review some of the theory behind the choice;
(c) in connection with the theory, define the assumptions of the model;
(d) describe the components of the model and its procedures;
(e) explain how findings are produced and present them;
(f) comment on implications and write a short conclusion.

READING MATERIALS
There are a few sources that can be used at various stages of the course and that will drive you through the course. Details on when and how to use the sources will be provided in class:

Fioretti, G. (2013). Agent-based simulation models in organization science. Organizational Research Methods, 16(2), 227-242.
Fioretti, G., & Lomi, A. (2010). Passing the buck in the garbage can model of organizational choice. Computational and Mathematical Organization Theory, 16(2), 113-143.
Gilbert, N. (2008). Agent Based Modeling. Thousand Oaks, CA: SAGE.
Neumann, M., & Secchi, D. (2016). Exploring the new frontier: computational studies of organizational behavior. In Secchi, D., & Neumann, M. (Eds.), Agent-Based Simulation of Organizational Behavior (pp. 1-16). New York: Springer.
Secchi, D. (2017). Agent-based models of bounded rationality. Team Performance Management, 23(1/2), in press.
Secchi, D. (2015). A case for agent-based models in organizational behavior and team research. Team Performance Management, 21(1/2), 37-50.
Secchi, D., & Gullekson, N. L. (2016). Individual and organizational conditions for the emergence and evolution of bandwagons. Computational and Mathematical Organization Theory, 22(1), 88-133.
Troitzsch, K. G. (2013). Historical introduction. In Edmonds, B., & Meyer, R. Simulating Social Complexity. A Handbook (pp. 13-21). Heidelberg: Springer.

Online resources (software):
NetLogo homepage: https://ccl.northwestern.edu/netlogo/index.shtml
Download NetLogo: https://ccl.northwestern.edu/netlogo/download.shtml
NetLogo dictionary: https://ccl.northwestern.edu/netlogo/docs/

Convenzionale

Borrowed from

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