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PhD Program in: Based Public Planning, Policy Design and Management

BACKGROUND

The public and private sectors consist of the political and the administrative levels that

regulate and offer services to the society. This creates a public context for the private

households and for enterprise development and operation. This private sector feeds back

to the public sector: public opinion is primarily affecting the political level, and income

primarily affects the funds that the government will be able to raise through taxes and

other sources to finance public expenditures.

System dynamics (SD) has proven an effective method to make explicit decision makers’

mental models as a way to identify discrepancies and to induce a fruitful dialogue between

parties such as the actors in the public sector and between them and those in the private

sphere. Such a dialogue is the prerequisite for building mutual understanding, confidence

and trust between these parties and to establish a foundation for organizational learning, a

key component in organizational development.

The conceptual framework to which the above challenges can be referred is the so called

“New Public Management”.

THE FALLACIES OF LONG-TERM PLANNING AND THE NEED OF A LEARNING-

ORIENTED APPROACH.

In the last decade there has been an increasing pressure to increase efficiency and

effectiveness in government. Under the label of New Public Management, governmental

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and other public institutions have been promoting new approaches aimed to support

decision makers’ accountability and choices, both on the political and managerial levels.

Such efforts have been specifically oriented to provide organisations with formal Planning

& Control (P&C) systems, aimed to support performance measurement, benchmarking,

and a better allocation of resources.

Different kinds of models have been used to support effective planning and decision

making in organisations. For instance, accounting models can provide an analytical view of

the system of values portrayed in a balance-sheet, while econometric models can suggest

decision makers “optimal” policies to implement in order to minimize or maximize a given

“objective function” referred to a well-enough known system.

Although there have been positive experiences in the world in this area, quite often the

introduction of formal P&C systems have not lived up top the expectations or have been

producing unintended side-effects, particularly with regard to:

an increase of bureaucratization;

a missing connection between the political and managerial levels (leading to lack of

coherence between strategic, managerial and operational goals);

a poor definition and alignment of goals, activities, and results (in terms of

performance indicators);

a static and bounded view of the relevant system for public policies and

management decisions, leading to schizophrenic behaviour.

Such an approach to P&C systems design and implementation is highly mechanistic and

implies a high risk of manipulation in goal setting and performance evaluation.

The shortcomings of the described perspective are particularly relevant due to the

increasing complexity and unpredictability of the systems in which public policies are

implemented.

Long-term planning has often been considered as a proper response to complexity and

unpredictability. It is often conceived as a way of reducing uncertainty through prediction.

Decision-making based on such an approach does not necessarily lead to learning how to

anticipate possible outcomes. On the contrary, it gives an illusion of control.

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Many organizations use simulation tools to foresee the financial implications of plans.

Quite often, such analyses are based on simplistic and misleading hypotheses that can

lead decision makers to dangerous conclusions. In fact, usually such tools do not make

explicit interdependencies between relevant variables, delays, non-linearities and policy

levers.

On the contrary, through scenario analysis, decision-makers are able to focus on the

relevant environment and to generate a set of alternative outcomes, upon which a

strategic analysis and diagnosis will be developed.

Consequently, a learning-oriented, rather than bureaucratic, approach is needed. After all,

it is only through systematic learning that policies can become more effective. That means

learning both before and after policies have been implemented. Currently many policies

have a large trial and error component, “imperfect” learning takes place while manipulating

the real system. But learning from reality is fraught with problems, e.g. while distortion of

information takes place and policy makers mental models are not well equipped to

understand the behavior of complex systems.

In order to frame, i.e. to understand, the structure of systems where decision makers

(politicians and managers) operate, and to foster a better communication, SD models can

make a significant contribution.

According to the current approach, organisations are often inclined to define performance

indicators (e.g. related to volume, efficiency, outcome, productivity measures) without a

proper understanding of administrative processes, strategic resources and policy levers on

which to act in order to affect their performance/behavior over time. In this respect, SD

modeling can enable policy makers and managers to better understand how to affect

performance and how to properly gauge it.

Another implication requiring the use of more systemic approaches and tools in

organizational planning and decision making processes is related to the need of framing

the relevant system, which is very often extended far beyond the legal boundaries of a

single organisation. Some examples are: Tourism, Public Works, Industry, and Commerce.

Very often, in these and many other contexts defining goals/objectives and assessing

performance in a Governmental institution requires an effort to understand how the

provided service will impact on the management processes fulfilled by other public and

private entities (e.g. local autonomies and companies, respectively). This also implies the

need of a strong collaboration, encompassing several institutions.

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Therefore, a new professional profile is needed to improve knowledge about organizational

strategic and managerial processes.

A PhD focused on SD modelling to support planning, policy design and management in

organisations can give an important contribution to both develop new researchers in such

a field and to provide Public Institutions with experts who are able to facilitate

communication and learning processes in such contexts.

The expertise that will be gained through the PhD will also allow students to build relevant

skills for private enterprises. In fact, also in business organizations there are financial and

organizational constraints, due to resource scarcity and to the reluctance of different

departments (R&D, Finance, Commerce, Marketing etc.) to cooperate each other

according to a systems view.

GOALS

The primary aim of the PhD is to prepare students for research and teaching in the area of

public and private sector growth planning and crisis management.

Such an expertise will allow students to make strategic analysis and diagnosis, leading to

plan strategies aimed at counteracting weak signals of crisis and therefore foster a

continuous improvement of processes, both from a qualitative and quantitative

perspective. This expertise is gained through a systemic view of relevant variables

pertaining to the policy problem.

Such view can be pursued by teaching experts to detect:

strategic assets, i.e. resource stocks such as production capacity, knowledge,

image, liquidity, equity;

performance drivers and outcome indicators

main delivered “products/services” and underlying processes and activities fulfilled

by different responsibility areas;

feedback loops and non-linearities underlying accumulation and depletion

processes which have an impact on strategic assets' dynamics;

delays between causes and effects;

policy levers on which to act to foster policies aimed at solving crises and enhancing

growth;

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monetary and non-monetary factors impacting on performance;

internal and external variables impacting on organizational phenomena decision

makers wish to affect.

The above approach will allow organisations to acquire the needed knowledge and skills in

order to build - together with main decision makers - SD simulation models, aimed to

support learning and decision making processes underlying public and private growth

management.

Public and private organisations will also be able to link SD with accounting models. The

latter models provide the backbone for planning & control systems. By linking SD with

accounting models decision makers are enabled to implement effective and learning-

oriented strategic control systems, and are empowered to draw up dynamic performance

management-oriented plans.

PROGRAM FOCUS

The Doctoral programme is specifically oriented to public and private sector participants

aiming at:

a) starting a career in Universities and Research institutions, or even in “think tanks”

involved in organisations analysis;

b) working in Public Administration (ranging from Governmental Institutions, Counties,

Municipal administrations, Public utilities, Health care organizations, etc.);

c) working in Private Sectors

d) supporting, as consultants, organisations’ decision makers in better assessing the

quality and sustainability of their policies and strategies.

Participants will learn how to conduct a research in the context of P&C applied to an

organisation’s strategy.

Attending the program will also allow students to effectively operate in (and on behalf of)

banking and other rating institutions. It will also allow them to better support public

and private organisations in drawing up plans, in:

evaluating the sustainability of growth strategies,

detecting weak signals of crisis,

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outlining reorganizational strategies, aimed at resolving crises.

Scenario planning can be successfully implemented through the use of SD simulation

models, linked with financial models, based on an accounting perspective. The SD

methodology allows decision-makers to make their mental models explicit, to assess their

consistency and improve them.

Computer-aided tools, based on the SD methodology, are not designed to provide

accurate predictions of the future; they are a realistic and engaging vehicle to stimulate

managers into reconsidering alternative ways of doing things, and to adjusting their mental

models. Different organization’s stakeholders can then compare and share their new

emerging views of, for example, how to prepare for change.

Through facilitated learning sessions involving decision makers, organisations, participants

will be enabled to sketch dynamic scenario plans supporting strategic control systems.

SD models they will build will provide powerful learning vehicles for performance

evaluation through dynamic balanced scorecards.

Particularly in the last decade, several public utilities have been operating using

continuous monitoring qualitative key-measures.

Such performance measures not only relate to the financial dimension, but also to 'soft'

parameters such as quality, service, company image, and internal process efficiency. At

least in the long run, all those 'soft' parameters tend to dramatically impact on financial

results.

Generally, such indicators are not focused enough by conventional accounting tools (as far

as they might be sophisticated), since they cannot be gauged in financial terms. However,

monitoring the dynamics of non-monetary performance indicators is a necessary step to

understanding the company's attitude to satisfying the needs for which it exists. Such an

attitude is a pre-requisite for achieving financial targets and long term survival and growth.

For instance, in a public utility company, such as city water supply, a systemic view of

those indicators allows decision makers to:

1. Assess managerial efficiency and effectiveness;

2. Detect and set policy levers to improve performance;

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3. Outline targets referred to different decision areas and link them to compensation

and career systems;

4. Discern causes related to unexpected results, and distinguish the controllable from

non-controllable ones;

5. Provide an important tool to communicate company's social strategy to several

stakeholders (e.g., citizens/customers, political counterparts) in order to clarify

undertaken policies, and to promote company's commitment towards efficiency and

effectiveness;

6. Linking SD models to balanced scorecards which are likely to enable decision

makers to gain a systemic view of main monetary and non-monetary variables

impacting on organizational success. To understand and manage feedback

relationships between variables to be monitored, it is, however, necessary to

analyze:

a. cause and effect relationships between the different variables affecting

performance indicators;

b. policy levers through which it is possible to act, to improve results depicted by

financial and qualitative indicators;

c. delays between causes and effects;

d. relationships between performance indicators, the budgeting process and

rewarding mechanisms.

WHY TO ENROL:

Participants will improve their capabilities and learn distinctive knowledge in studying and

approaching organisations. In particular they will learn how to:

• manage organisational restructuring & growth

• improve better communication between politicians and managers, among managers,

between politicians and citizens, between different public institutions and between a

single public institution with private ones

• identify key strategic resources coherently with strategy

• effectively communicate objectives and identifying appropriate performance indicators

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• link the budget to a coherent picture of monetary and not performance indicators

(Balanced Scorecard)

• draw up a Dynamic Plan.

They will also:

• experience in public and private companies the benefits of a systemic approach, and

• learn how to effectively support public managers in linking political and management

objectives, between past, present and future.

PROGRAM OUTLINE:

The PhD consists of three academic years, corresponding to 180 credits, during which

students will attend seminars, lectures, focussed modelling and simulation sessions, class

discussion sessions, computer based training sessions.

The teaching strategy will be based on the active participation of students and on the need

to increase their attitudes to frame a scientific problem, develop research hypotheses,

adopt proper research methodologies to test them, and evaluate results.

Such a research method will be deeply rooted in an interdisciplinary perspective that will

be fostered by the SD methodology. Furthermore, students will learn how to develop and

implement their research strategy in accordance with key-actors (i.e. main decision

makers) in the relevant system, in order to improve their learning processes. Therefore, an

empirical perspective will be pervasive throughout the program: in particular, among the

teaching sessions will be: role playing, modelling & simulation, and real cases/examples

provided by managers who will be invited to illustrate their own experiences and

perspectives of reality. In the second year, students will be also involved in tailored

projects, focussed on concrete cases, with the aim to learn modelling skills to support

public planning, policy design, and management.

For each teaching hour, students will devote four hours to self study, covering different

domains, ranging from: literature analysis, writing (and dissertation) of papers, the setting

up and delivery of lectures, etc. Throughout the program, the PhD faculty will provide

students tutoring on all the activities covered by self study.

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The first year will consist of the following courses:

FIRST SEMESTER:

a. Dynamic Performance Management I (Planning & Control Systems) – 10 credits: The course aims to provide students the fundamental concepts related to the design and

implementation of Planning and Control (P&C) systems. A specific focus is given to the

implications of designing P&C systems in public sector organizations. The goal of the

course is also to allow students to gain a systemic perspective on how to design and

implement P&Cs which are capable to support organizations to act across several

disciplines or professional specializations, such as: Accounting/Planning/Reporting;

Strategy; Organization & Human Resources; Systems Analysis.

Designing responsibility areas, linking them to performance measures, and understanding

behavioural implications associated to formal and informal performance management

systems are an important issue that is focused in this course. A “learning-oriented”

perspective in P&C systems design and implementation is adopted.

Students gain knowledge about the fundamentals of designing P&C systems to support

the steering and management processes of different organizations operating in the public

management context. They gain a systemic and design-oriented view of P&C.

Students specifically learn about the factors of complexity particularly influencing and

characterizing the planning, policy design and management in the public sector. They will

also know how to apply the fundamentals of P&C design to public sector organizations, in

order to support their governance and management processes.

They also learn to analyze and diagnose organization’s solvency and liquidity, and to draw

up plans that reflect the dynamics of the public and private sectors.

The students will engage in real life case-study analyses that will be conducted, in which

they will apply their knowledge and understanding acquired from the field of P&C,

facilitated through the use of SD mapping.

Students should be able to reflect on the method to use while adopting planning and

control systems as a viable means to foster empowerment, accountability, communication

and learning, particularly in organizations operating in a complex and dynamic

environment.

Students will present and discuss relevant literature as well as the result of their case

studies in class.

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Students will acquire skills that are required for self-studies of the literature on the subject

and to investigate the relationship between Planning & Control and systems performance.

The course is divided into three parts: 1) Principles and techniques for P&C Systems Design

- Planning & Control as a System; - Different levels of control; - Levers of control - Organizational control - Designing P&C systems vs. Organizational Design - Defining performance – Outlining goals objectives and performance

indicators. - Linking objectives & performance indicators to strategic resources, policy

levers, responsibility areas, and management processes - Designing P&C systems: Common errors

2) Contextual and Behavioural Implications of P&C Systems in the Public Sector

- Specific complexity factors in public sector organizations. The applicability of management principles to public sector organizations

- Development levels of strategies in public sector organizations: government and management

- From a bureaucratic to a managerial view of Planning & Control in the public sector (input; process; output; outcome): The New Public Management vs the New Public Service view.

- Designing Planning & Control Systems in the Public sector: from a structured to a learning-oriented approach

- On Responsibility centres, information tools, and the control process in the public sector.

- Designing Planning & Control Systems in the Public sector: from a structured to a learning-oriented approach

- On Responsibility centres, information tools, and the control process in the public sector.

- Legislation frameworks concerning planning & control in the public sector - Cultural constraints in implementing Planning and Control Systems in Public

Administrations - Benchmarking Public Services - Formulating objectives, activities and performance indicators: the strategic

and operational plans – Case-study analysis - Designing P&C in the Public sector: from an organizational (institutional) to an

inter-institutional perspective - Behavioural implications of performance management systems in different

industry areas (e.g. police and public safety, health care)

3) Tools for business solvency & profitability analysis: an introduction - Financial analysis: ratios - Profitability & Solvency analysis - Financial analysis: flows

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- Assessing solvency, liquidity and profitability in relation to sustainable growth. - Cost analysis - Contribution margin analysis - Budgeting and variance analysis

b. Dynamic Performance Management II (Business Strategy) - 10 credits: the course

aims to provide an introduction to the SD for business strategy, with a specific focus on

small medium enterprises and on their relationships with the public sector.

Students gain knowledge in the application of SD to business strategy formulation and

implementation, with a particular focus on small medium enterprises and on their

relationships with the public sector. Skills are developed in mapping processes affecting

performance. Students also learn to use the SD method in supporting business decision

makers to identify areas for performance improvement, and set proper goals/objectives, as

well as performance indicators to foster sustainable strategies.

The students will engage in real life case-study analyses in which they will practice their

business knowledge, modelling skills and systems understanding on identifying the

systems structure underlying poor business performance and on developing and

assessing strategies and policies aimed at performance improvement. Students will

demonstrate their ability to transfer their skills across management disciplines, including

strategic management accounting and business and scenario planning. And they learn to

approach a problem from a multi-sectoral and a multi-disciplinary perspective in the private

vs. public sector domain.

Students learn to assess the sustainability of business strategies from various

perspectives. They gain a systemic, time-related, and context sensitive view of firms. They

also learn to evaluate performance, based not only on financial, tangible and intangible

factors. Management is considered the integration of planning and control, strategy and

implementation resulting in organizational learning. Students learn to detect the limits of

conventional methods, techniques and tools in strategy design and implementation, and

performance evaluation. They learn how the role of such tools can be re-shaped according

with an emphasis on learning using SD. Real case-study analysis will be conducted

during lectures.

Students can present and discuss relevant literature sources as well as the result of their

case studies in class. They also present results from modelling and simulation sessions to

stakeholders in organizations and to interested academics.

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Students are enabled to acquire skills – also through individual assignments (together with

feedback from teachers) – that are required for self-studies of the literature on the subject.

The course is divided into three parts:

1. Strategy principles & Strategic planning/control - The concept of business strategy – The Business Idea. Strategy as learning - Strategy as learning: Case-study analysis - Strategic Planning & Control. Conventional strategic analysis tools: matrixes,

SWOT analysis - The strategy process

2. Using P&C tools for strategic analysis and diagnosis - Financial analysis: ratios - Profitability & Solvency analysis - Financial analysis: flows - Assessing solvency, liquidity and profitability in relation to sustainable growth. - Cost analysis - Contribution margin analysis - Budgeting and variance analysis - Framing financial statements through SD models

3. Business Growth Sustainability & restructuring strategies - The specific complexity of small-medium enterprises (SMEs) - Planning for business growth and restructuring - Modelling SME Growth - Diagnosing Business Growth Sustainability - Modelling stunted and inflated growth - Modelling Intellectual Capital - Designing & Implementing ILEs to support management education and

entrepreneurship.

c. Dynamic Performance Management II (Public Management) - 10 credits: the course

will focus the complexity factors that particularly influence and characterize planning,

policy design and management in the public sector.

Three Dynamic Performance Management (DPM) perspectives are analyzed: an

instrumental, an objective and a subjective DPM view.

Students learn to analyze problems at different consequential levels, i.e. departmental,

political, interdepartmental, cross-institutional. The need to link the political and managerial

level, planning and control, design and implementation, policy formulation and evaluation

is emphasized. The benefits of joined-up government are explored, and linked with the

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need to frame the value chain leading to deliver ‘products’ to citizens, through the

fulfilment of processes and activities. Improving service quality and operational efficiency

are analyzed as primary outcomes of more ‘learning-oriented’ P&C systems, according to

a ‘New Public Management’ perspective in the public domain.

Students also learn how to adapt the SD method as an approach to foster a ‘learning-

oriented’ view of Planning and Control in the public sector. They learn how to relate SD

models coherently and consistently to other Planning and Control models to better support

key-actors’ learning and decision making in and across various public domains.

Students develop SD models and Interactive learning Environments (ILEs) to facilitate

effective planning, control, policy design, strategy development, and implementation in

various public contexts. More specifically, such knowledge will be applied at three levels,

i.e.: a macro, meso, and micro level. The first one relates to contexts that may imply the

need to model various inter-related sectors of the economy and to support decision

making concerning different ‘key-actors’, often operating across several institutions.

Applying SD modelling on a meso level implies the opportunity to analyze problems from

the perspective of a sector, i.e. in a view that is usually adopted by different branches of a

public administration (e.g. a Ministry). Applications of System Dynamics modelling at these

two levels address the political processes. Applications at the third level (i.e. the micro

one) address the departmental or managerial processes. In fact, it focuses on the analysis

of ‘administrative products’ that are delivered by the fulfilment of processes and activities

inside the department of a given Ministry. In developing SD models addressing all the

three levels, students learn to: (1) use SD as a method that portrays the tight relationships

that exist between the managerial and the political level; (2) use SD as a method to

support the development of Planning and Control systems, - e.g. in defining performance

standards, gauging results, analyzing performance drivers, outlining strategic resources,

identifying policy levers, - all within the framework of the ‘dynamic’ balanced scorecard

perspective.

The students will engage in real life case-study analyses in which they will practice their

public sector and modelling knowledge and understanding on public management

disciplines. They will identify the systems structure underlying poor public performance

and will develop and assess strategies and policies aimed at performance improvement.

Students will also analyze how to assess and manage sustainable development.

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Students will demonstrate their ability to transfer their skills across management

disciplines and public sectors and will learn to approach a problem from a multi-sector and

a multi-disciplinary perspective.

Through SD based case-study analyses, students learn to assess the sustainability of

public policies and strategies from various perspectives. They gain a systemic, time-

related, and open-ended perspective on public organizations. They also learn to evaluate

performance, based not only on financial and tangible factors, but also on intangibles.

Planning and control, and strategy development and implementation are considered

elements of an integrated approach aimed at fostering decision makers. Students learn to

detect the limits of conventional approaches (theories, techniques and tools) for policy

design, strategy development and implementation, and performance evaluation.

They should be able to reflect on the method to use in order to adopt Planning and Control

systems as a viable means to foster empowerment, accountability, communication and

learning, particularly in public organizations that operate in a complex and dynamic

environment. Different levers on which to act in order to affect radical change in public

organizations are examined according to various managerial “schools”, ranging from the

Reinventing Government to the New Public Service approach.

By experience they recognize the values and the limits of the SD method, when applied to

performance management systems, and are inspired to reflect on how that method can be

used for learning purposes.

Students can present and discuss relevant literature sources as well as the result of their

case studies in class. They also present results from modelling and simulation sessions to

stakeholders in organizations and to interested academics.

Students are enabled to acquire skills that are required for self-studies of the literature on

the subject.

The course is divided into three parts:

1. Designing Dynamic Performance Management Systems in Public Sector organizations

-­‐ An instrumental view -­‐ An objective view -­‐ A subjective view 2. Applying Dynamic Performance Management to the public sector on a different

scale: a macro, meso, and micro level -­‐ The role of SD modelling in supporting planning, control, performance evaluation, and

decision making, in a strategic learning-oriented approach. SD modelling and joined-up government

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-­‐ The support of SD modelling to frame the relevant system by comprising both public and private sector decision makers

-­‐ Different perspectives and application domains for SD modelling in the public sector: macro, meso and micro views. Applying SD in a macro perspective: an inter-institutional Territorial perspective

-­‐ Applying Dynamic Performance Management (DPM) in a macro perspective: planning in State, Region, and Municipal institutions

-­‐ Applying DPM in a macro perspective (cont’d): supporting the setting of goals/objectives in State, Region, and Municipal institutions

-­‐ Applying DPM in a macro perspective (cont’d): supporting the undertaking of actions in State, Region, and Municipal institutions

-­‐ Applying DPM in a macro perspective (cont’d): supporting strategic monitoring and feed-forward mechanisms in P&C systems in State, Region, and Municipal institutions

-­‐ Applying DPM in a macro perspective (cont’d): supporting performance evaluation in State, Region, and Municipal institutions

-­‐ Applying DPM in a meso perspective: linking political goals with managerial objectives. Matching short with long term performance

-­‐ Applying DPM in a micro perspective: focusing departmental objectives, activities, and performance measures. Focusing strategic resource dynamics at departmental level, to affect performance

-­‐ Applying DPM in a micro perspective (cont’d): allocating resources and measuring performance using scenario analysis at departmental level. Balancing activity levels affecting different departments in a same Ministry, to affect service quality and efficiency

3. Developing Dynamic Performance Management to foster customer satisfaction, performance improvement and accountability in the public sector

-­‐ Urban planning and sustainable development -­‐ E-government -­‐ Industrial networks

-­‐ Modelling the value chain of delivered services in an inter-institutional perspective -­‐ Modelling products, processes, and related performance measures -­‐ Public Works (laboratory) – Case-study -­‐ Energy (laboratory) – Case-study -­‐ Education (laboratory) – Case-study -­‐ Social services (laboratory) – Case-study -­‐ Public Utilities - water provision (laboratory) – Case-study -­‐ Public Utilities garbage collection – Case-study -­‐ Police and Safety – Case-study

-­‐ Back-office units - Managing Billing Processes in a Municipal Water Company: A Dynamic Balanced Scorecard Perspective.

-­‐ Back-office vs. Front office units service delivery – one-stop-shop service -­‐ Health Care - Case-study

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-­‐ Labour and unemployment policies – Case-study -­‐ Environmental Protection Agency – Case-study

-­‐ Education – University Management – Case-study -­‐ Culture - Dynamic Balanced Scorecards in Theatres (laboratory). Case-study -­‐ Tourism - Case-study

SECOND SEMESTER:

a. System Dynamics I – 15 credits: This course aims to develop students’ proficiency in

the application of theories, methods, techniques, and computer-based tools. The tools

include software for systems modelling, simulation and analysis, for building simulation-

based interactive learning environments and to construct laboratory experiments. In

particular, it aims to show how the goal of model-based analysis is to identify the

underlying causes of current and potential problems, to explain these in an intuitive and

graphical language, and to spell out the consequences of good and bad policies. It is

also emphasised the importance of analysis and communication to properly map

systems. Students learn how the same modelling principles and the same methods of

analysis apply whether one investigates problems pertaining to natural resources,

national economies, developing countries, medicine, management, psychology etc.

Thus, students gain an ability to transfer knowledge from one field to another, and they

become potential leaders of multidisciplinary work. The program’s strong problem focus,

realistic cases, and project work also give the students experience in designing and

carrying out project work. Modelling and laboratory experiments contribute to a deep

understanding of “bounded rationality”. Students also learn how to design and use

Interactive Learning Environments based on SD models. An Interactive Learning

Environment is an instructional resource, which has several important characteristics. It

situates the user in a simulated “arena” replicating a real world problem, providing a

safe environment for testing strategies and policies. Learners are encouraged to design

strategies, implement them, observe their simulation results, and to comment on how

their decisions and policies influenced the results user interface provides reports,

graphs and tables to show the results of an interactive simulation. Often, the challenge

for learners is not only to sustain corporate activities, but also to earn a reasonable

financial return from the implementation of their strategic plans. Questions that learners

should be able to answer at the end of the learning process are: 1) How can they meet

both, short-term growth and long term sustainability, pressures? 2) Are they able to

design ‘robust’ policies that serve the market in quantity, satisfy customers in quality,

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take care of the environment, develop the organization, and maintain their ability to

obtain sufficient financial resources?

More particularly, this module consists of two parts:

a.1 System dynamics modelling: In this parts, the students develop advanced modelling skills, and they get experience in conducting and presenting the results of a modelling project. It covers advanced topics in model formulation, analysis and validation, policy design and evaluation, and strategy development, based on a variety of methods, techniques, and tools.

a.2. Interactive Learning Environments: In this part, the students will be offered

practical guidance in creating interactive learning environments within the field of SD. The students will gain experience in using software to develop both single- and multi-user learning environments. They will, individually or in groups, develop their own learning environments that they document with focus on (1) purpose, (2) the underlying model, (3) reasons for the design, including theory, (4) and user guidance. Their work is evaluated with respect to purpose, documentation, transparency, effect evaluation, and presentation.

b. System Dynamics II - 15 credits, This course will consist of three parts:

b.1 Research Design. The part covers fundamental concepts and techniques in research design, problem formulation, execution, and analysis, stressing applications in public policy.

b.2 Introduction to model based analysis and policy design: The part teaches the four basic elements in all dynamic systems: stock-and-flow relationships, reinforcing and balancing feedback, nonlinearities, and major feedback loops with delays. These basic building blocks give rise to behaviours such as exponential growth, s-shaped growth, cycles, overshoot, and chaos. Each basic element is introduced by a classroom experiment and ends by often surprising policy insights. Analysis is performed by graphical techniques and simulations and not by the complex mathematics used in the hard sciences. Finally, the course presents a series of examples such as renewable resources management, behaviour of commodity markets, demographics, epidemics, information diffusions, pollution problems, physical processes and organizational applications. When the basic building blocks are understood, learning becomes both deeper and faster than otherwise. Hence the course is very different from traditional teaching which does not use a unified representation of structure and which does not attempt to explain behaviour.

c.3 Model based development planning: Developing countries often struggle with long lasting, comprehensive problems that seem immune to quick fixes. This part addresses the challenges of identifying the roots, i.e. structural foundations, of such problems and policies that may work to solve such problems. Central in this course is a SD model for development planning called Threshold 21. This model has received favourable evaluations from the United Nations Development Program and has been used by many development countries, international agencies and major corporations in comprehensive studies of national development. The model reflects the macro economy, social issues and the environment, including natural resources.

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The second year will consist of the following courses:

1st Semester:

a. Advanced Dynamic Performance Management (Applied Project under supervision of Faculty) – 30 credits Special attention will be devoted to developing SD models focussed on different

application fields related to the public sector, such as: Energy, Health Care, Mapping and

modelling internal processes and service delivery, Urban Dynamics, Labour Markets,

Crime, Environment, Fisheries, Industry, Tourism, Waste, Agriculture, and Territorial

Strategic Planning.

During this semester, students will be enabled to attend focused seminars on the following

themes:

a.1 Institutional issues in Public Administration. Seminars will debate why public organizations exist, along with their relationship to the environment and explains how they manage or administer public policy and programs. They will also examine current governmental management practices, looking at their theoretical bases and at empirical evidence about their efficiency effectiveness. Among the covered topics are: an analysis of public institutions as organizations, peculiarities of public sector organizations and commonalities with private/for profit organization. The “New Public Management” concept is also explored, and the ‘accountability’ and ‘service’ principles are emphasised.

a.2 Strategy in the Public Sector. Seminars address strategies and issues relating to the strategic management of public and quasi-public organizations. They address strategic planning and performance measurement processes within organizations. They also focus the issue of strategy design and development in the public sector, as well as the causes of recent shifts in public management theory.

a.3 Ethics in Public Administration. Seminars aim to help students discover the ethical implications of both political and managerial decisions and actions in the public sector. It is shown how a professional or a politician cannot only be considered as good only because of his/her own expertise, but also because of adherence to ethical standards. In particular, the relevance of such standards is analysed in the perspective of a “full” and comprehensive growth of the public organization, which is considered as part of a wider system. The course also provides maps and tools to make moral experiences more explicit and consistent. Individual decision-making strategies and organizational programs to address challenges are explored. Case studies of managers who confront ethical dilemmas as well as management issues such as workforce diversity and quality improvement complement this material.

a.4 Theories and Economics of Development. The objective of these seminars is to develop an understanding of the major theoretical debates that have influenced economic and social policy in developing countries over the last decades. Such an understanding is crucial, because the new forms of economic and social organization that are emerging in our increasingly globalized world economy are fundamentally challenging the ways in which development has been conceived. These seminars will also familiarize students with a variety of

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alternative theories on what causes (or hinders) economic development. Different strategies and outcomes from a variety of settings will be presented and discussed. Students will improve their understanding of international, national, sectoral, local, and household level issues related to economic development. Special attention will be given to the following questions: Are there differences between economic growth and economic development? What are the environmental implications of economic development? and How are industrial/urban needs balanced against agricultural/rural needs in development?

a.5 Organizational Theory & Behaviour in the Public Sector. The seminars will illustrate: rational choice and bounded rationality theories of public management; organizational culture and worker-incentive structures; privatization in theory and practice; and empirical evidence on the effectiveness of the “new public management”.

2nd Semester:

a. Group Model Building I - 15 credits. This course will be given at the University of Nijmegen. It aims to show how the success or failure of an organization depends very much on group performance, which is decisively influenced by the different perspectives individual team members have on the situation and the resulting differences in defining problems. During the course, students will explore the limits to the cognitive capacity of individuals and the growing obsolescence of bureaucratic structures as problems that affect group performance. Also the possibilities of facilitating team learning will be examined, focusing on the building of SD models, with the aim of sharing mental models and avoiding conflicts inherent to the process of group decision-making. From this point of view, group model building is regarded as an organizational intervention process designed to enhance team performance and the ability of team members to learn from each other under the conditions of increased complexity in the decision making process. A variety of topics related to group model building will be discovered, e.g., human information processing, the construction of SD models with key decision makers, individual and team learning, group processes and group facilitation. The module will also present tools and methods that help to obtain productivity in complex situations and to foster consensus.

b. Group Model Building II - 15 credits. Skills learned by students in the first module will

be applied on the field through real cases.

During this semester, students may also attend specific elective courses on:

Thesis proposal writing: this course aims to give students methodological support to write their own thesis proposal. Central issues are problem formulation, motivation, description of methods, expected results as well as project planning.

Laboratory experiments and bounded rationality: The course enables the students to design and carry out laboratory experiments of dynamic systems. The experiments and the accompanying literature provide valuable insights into bounded rationality in general and misperceptions of dynamics in particular. Theory and methods for the design, performance, and analysis of laboratory experiments regarding dynamic problems are provided. The course includes an introduction to optimisation for benchmark estimation and of statistics for hypothesis testing. The students should design and carry out their own

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pilot laboratory experiments with focus on (1) purpose, (2) hypotheses, (3) design, and (4) analysis.

The third year (60 credits) will be devoted to the development of a PhD thesis under the

supervision of one of the professors of the teaching Faculty or of a professor working in

another University together with whom a research program has been outlined and

approved by the PhD committee.

The following table summarises the PhD curriculum:

1st Year 2nd Year 3rd Year

1st Semester

Dynamic Performance Management I Planning & Control Systems (10 ECTS) Dynamic Performance Management II

Business Strategy (10 ECTS) Dynamic Performance Management in the Public Sector (10 ECTS) (Students from the

partner University must take the courses stated above at the University

of Palermo)

Advanced Dynamic Performance Management (Applied Projects under Supervision of Faculty, and seminars on focused Public Management topics) – 30 credits

2nd Semester

System Dynamics I – 15 credits System Dynamics II – 15 credits

(Students from the University of Palermo will take the courses stated above at the University of Bergen. If they have at least a 2-years study background in System Dynamics, they will focus their semester in Bergen on Applied Projects under

Group Model Building I – 15 credits Group Model Building II – 15 credits

(Students from the University of Palermo will take the courses stated above at the university of Nijmegen or other partner University)

Thesis writing

(Under the supervision of a

professor from the University of Palermo and a professor of the

partner University, and based on a co-tutelle agreement)

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Supervision of the University of Bergen Faculty)

SCIENTIFIC COORDINATION

• University of Palermo: Prof. Carmine Bianchi,

• University of Bergen: Prof. Pål I. Davidsen,

• Nijmegen School of Management, Radboud University: Prof. Jac Vennix.

ADMISSION REQUIREMENTS The PhD in “Model Based Public Planning, Policy Design, and Management” requires the

completion of 180 credits, in three years.

Entering students should have completed a Master of Science (preferably on Management

Sciences), and demonstrated proficiency in oral and written English. Only “full time”

students will be admitted to the program.

PHD OVERSIGHT COMMITTEE

Students are evaluated by a PhD Oversight committee, which is made up of Faculty

members of the Universities linked in this PhD programme.

STUDENTS EVALUATION

In order to be eligible for admission to the double PhD degree programme in “Model Based

Public Planning, Policy Design and Management”, each candidate must carry out their

studies within the framework of a co-tutelle agreement between the University of Palermo

and the foreign partner University. In that agreement, an exchange program for at least

one teaching semester will be ensured for every student.

Such an agreement will be signed by the candidate and the PhD candidate advisors from

both the University of Palermo and the partner University. The agreement will include a

specification of the study plan and the responsibilities of both PhD candidate advisors as

well as the candidate.

PhD candidates will conduct their research activities under the joint supervision of a

professor of the University of Palermo and a professor of the foreign partner University.

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To be awarded the double degree, PhD candidates will defend their thesis at both the

University of Palermo and the partner University (via videoconference if convenient).

The PhD thesis will be discussed and defended in English.

The successful completion of the PhD degree in “Model Based Public Planning, Policy

Design, and Management” requires that students demonstrate competence through a

comprehensive examination covering the foundations of Performance Management; the

application of SD to different Public and Business domains, research methodology; and a

specialization area identified by the student and approved by the PhD oversight

committee.