SciE Block 1
Lecture 1 – What is economics aobut? (mo 4 sept)
This course is related to societal challenges we all face. We look at it from an applied
perspective rather than purely theoretical. (example = tragedy of the commons).
how to study: learn on a self-reliant basis (je wordt dus niet bij de hand meer meegenomen).
Tragedy of the commons: when ST self-interest of individuals, instead of the collective
interest of society as a whole, leads to tragedy for all. It’s all about SCARCITY (Schaarste).
- Material resources (Forest, oceans, Air) with no private ownership are used exploited.
(example = overfishing). Solution: future needs must be met. This can be met by
social norms, privatization and regulation.
- sustainable development: the meets of today are met but the needs of tomorrow
are also accounted for. Tragedy of the commons completely disregards sustainable
development.
There are 5 new technologies we will discuss: big data, machine learning, distributed ledger
technology, internet of things, and cloud computing.
Societal challenges are classified into 3 perspectives: biosphere, society, and economy.
- The Sustainable Development Goals (SDGs) as a construct of societal challenges.
The dramatic attention filter: makes the world look more dramatic than it is. Negativity bias -> glorifying the past & selective reporting in the media.
Job purpose of an economist: To analyse and interpret economic data and trends to provide
insights into economic relationships and advise on economic policy or business strategy.
Economics is about: Scarcity (limited resources), allocation (distribution of goods), market
(form of allocation), decision making (how to make decision), people (interaction of people)
- The study of individual decisions is called microeconomics. The study of the economy
as a whole is called macroeconomics.
- Economics: It’s the study of how people make decisions under scarce resources such
as time and money.
- So economics is about ecnomoy studies and economic decision-making!!
Economy studies: it’s a study of how economics education should be organised to best
prepare students fort heir future roles in society. Teach economics centered on a specific
subject matter: the economy.
- The economy: production, distribution and consumption of goods and services.
The economy nowadays need to go from the linear economy to a circular econony to reduce
waste. Circular economy (new way of looking to the economy): take less (renewables,
recycle), make less (repair, refurbish, remanufacture), use more (reuse, share redistribute!
Examples in the PP.
Recycle example: Aluminum cans are melted down and reshaped into new cans in a process
that requires significantly less energy than producing new aluminum.
BSE (biosphere, society, economy) framework: based on
Brundtland 1987.
The economy is part of a larger social system, which is itself
part of the biosphere. People interact with each other, and
also with nature, in producing their livelihood.
Biosphere: the global sum of all ecosystems on earth where
life is found (all living organisms including plants, animals,
and microorganisms, as well as the parts of the earth where
they thrive (land, water, air).
Society: a group of individuals involved in persistent social
interaction or a large social group sharing the same spatial or
social territory, typically subject to the same political
authority and dominant cultural expectations.
Ecological economics: transdisciplinary effort to link the
natural and social sciences broadly, and especially ecology and economics.
Lecture 2 (wed 6 sept)
De literatuur is meer voor een introductie en verduidelijking. De docenten geeft aan als de
literatuur verplicht is end it hoor je dan ook wel. They are more initial readings.
BSE framework: The economy itself is imbedded in the society. Economics as a discipline
works together with other disciplines.
Goal of economy studies is to understand the economy to alter economic interaction for an
improved society.
- How to distribute welfare and how to measure it? Here is where welfare
economics comes into play (welfare, well-being, happiness etc.)
o Utilitarianism is a normative ethical theory that prescribe actions that
maximize utility, happiness and well-being for all affected individuals (=the
society).
o Rawl’s governing principles --> Veil of ignorance is a moral reasoning
device designed to promote impartial decision making by denying decision
makers access to potentially biasing information about who will benefit
most or least from the available options. The philosopher John Rawls
aimed to identify fair governing principles by imagining people choosing
their principles from behind a ‘veil of ignorance’, without knowing their
places in the social order.
Simple consideration: (Cappelen et al. 2010) --> about fairness and individual responsibility.
- Individual contribution to society 𝑚𝑘  𝑀 = ∑𝑛 𝑚𝑘
- Libertarian view = everyone gets what she/he produced
- Egalitarian view = share equally
-
But people have different working times, different productivity, price per unit of
work, and luck (being sick). All these aspects might play a role on M
Based on these different facts, who deserves more or less than his/her
contribution?
People do not have the same opportunity to get rich… some are born rich… some have a
higher chance to enter the education system and chances to enter the job market (because
of different wealth classes). This is the ordinary risk of life: the destiny or is it just a random
draw that you have been born in a rich country.
Neo-classical economics:
- Rational agents maximize utility MR = MC (rational decision maker)
- Efficient allocation of resources due to market competition with free markets
- Normative model
- This is the benchmark!!!
- Assumptions of NC economics theory =
o Rational agents
o Marginal utility
o Relevant information
o Perceived value
o Savings derives investment.
o Market equilibrium
o Free markets
The book (dia 27) does not want to teach “to think like an economist”, and the teacher also
thinks that this view is too narrow of an economist, that it is too normative and has
insufficient pluralistic views because it suggests a very traditional picture of an economist.
Thinking AS an economist – structuring trade-offs – is a good idea (volgens leraar).
Nu gaat het verder over Economic decision-making --> Economics is about economic
decision-making! Decision = making is the process of identifying alternative courses of
action (ideal solution may not exist) and selecting an appropriate alternative (there may be a
number of appropriate alternatives) in a given decision situation. Thus, judgment is
fundamental to decision making.
You decide based on all the information you have:
1. beliefs how the world works; might be a gut feeling based on information you have.
Necessary to set the decision frame.
2. Preferences you have; things you want; dus waar je vrolijk van wordt (utility). A way to
rank alternatives.
3. Constraints there are and opportunity costs. What is feasible (scarcity = budget constraint
due to limits in time).
These 3 form a decision (think of a decision tree). This is called the BPC model! These are the
three things’ economists care about.
The cost of a decision = the actual costs + the opportunity costs. There is scarcity, this means
that there is always a tradeoff.
Marginal rate of substitution (MRS): the trade-off that a person is willing to make between 2
goods. at any point, this is the slope of the indifference curve.
Marginal rate of transformation (MRT): the quantity of some good that must be sacrificed to
acquire one additional unit of another good. at any point, it’s the slope of the feasible
frontier.
You can frame everything as an economic decision problem! (examples: go left or right,
marry or not marry, eat meat/fish or not, a doctor deciding on medication for a patient, a
politician deciding on what social security system to implement). The problem is: how to
measure these preferences (how to define the benefits and costs in terms of value).
How to define benefits and costs in terms of value --> econ101 = make use of utility.
Utility: reflects the preferences of a person regarding a good or a service (or other things?).
utility is a dimensionless number able to order choices in terms of ‘utiles’. When the utility of
a banana is higher than that of an apple, you will choose the banana. Utility is often
correlated with concepts such as happiness, satisfaction, welfare which are difficult to
measure. (how to measure preferences is the main problem).
4 areas of decisions (choices):
- Simple choice --> perfect substitutes (cola and Fanta) or perfect complements
(coffee and sugar). The more I go to the top right, the higher my utility.
- Intertemporal choice --> consume now or save for consumption later (Lucas Tree
model). Discounting future utility = utility of consumption in future is less relevant
than utility of consumption today. Life-time utility and quasi hyperbolic
discounting).
- Choice under uncertainty --> expected utility theory (don’t look at the expected
value itself; it’s about wealth) and the cumulative prospect theory (don’t consider
the full level of the outcome, but the difference in wealth; here we talk about
losses and gains). Ook regret theory (compare to forgone choice ‘y’) en salience
theory (some states are more salient than others) horen bij choice under
uncertainty.
- Choice with social effects --> the dictator game is a simple experimental paradigm
(distribute 10 between you and me), ECON101 outcome is to give nothing, but in
real life, people care about others and find it socially inappropriate, so they give
most often money. Give away everything is also not socially appropriate. The
prisoners’ dilemma is another choice with social effects (individual rationality can
lead to a collective suboptimal outcome). Public goods game (each person can
donate, and it will be added up and this is divided equally among the players) is
another choice with social effects. Also decision making for others, fehr-schmidt
and charness-rabin (without reciprocity) are examples of choice with social
effects.
Prisoner’s dilemma is widely used in economics to explain situations where individuals might
not cooperate, even when it seems like cooperation would benefit everyone.
How to understand economics decision making?
• Optimizing Individual (Micro 101, Decision Theory)
• Strategic Interactions (Game Theory)
• Market Interaction (Supply and Demand)
• System Interaction (Macro 101) (aggregate behavior)
To sum up: economics is about describing and understand the economy (economy studies)
and economics decision-making (2 part directions). But there are of course different
perspectives.
Different perspectives:
- Neoclassical economics (behaviour): allocation of scarce resources, determining
the efficient allocation of resources to increase welfare = utility of individual,
rational, non-social man.
- Behavioural economics (behaviour): deals with observing behaviour and
economic decision-making behaviour. Perspectives from psychology, social science
etc. (behaviour)
- Marxian political economy (power relations). Exploitation of labour by capital.
The MPE has developed historically out of asymmetric distributions of power,
ideology and social conflicts.
- Feminist economics (power relations). Interdependencies of gender relations and
the economy; care work and non-market mediated reproduction sphere.
- Institutionalist economics (systems). Role of social institutions (laws/contracts),
social norms and patterns of human behaviour connected to the social
organisation of production, distribution and consumption in the economy.
Institutions interact with each other.
- Complexity economics (systems). Interactions and interdependencies between
individuals and structures in complex economic systems. analysis of networks.
This is all about networks.
- Evolutionary economics (change & interaction). Focuses on economic change
(growth, innovation, structural and technological change).
- Ecological economics (change & interaction). Human economic activity is bound
by absolute limits. Interactions between the economy, society and the
environment are relevant for sustainability. Understanding economics decisions in
the environment where people live.
Goal oriented perspectives: (zie dia 56)
- Biosphere
- Society
- Economy (inequality between
companies and work life related
questions).
Econonomics is about: “The study of how people interact with each other and with their
natural surroundings in providing their livelihoods, and how this changes over time”.
New perspective that is popular: Doughnut economics: an economy is considered
prosperous when all twelve social foundations are met without overshooting any of the nine
ecological ceilings” (Kate Raworth).
- We should focus on the doughnut and not on just growth. Consider the
embedded economy, not a too abstract picture. Focus on the social/biased/shillyshally man (people are not only the rational economic man). Focus on the
dynamic complexity rather than the mechanical equilibrium. We should
redistribute to reduce the inequality and regeneratie circular economy to save
resources. We should also give up GDP growth as econ goal as it is too narrow
(there are other possitibility to measure it).
- It is rather a visual framework with a collection of goals and not a model. You
should emphasize the boundaries. It requires severe economy restructuring and
ambiguous targets So it’s not a model on systems, behaviour or markets you can
implement.
Lecture 3 – What are societal challenges? (mo 11 sept)
Societal challenges: (problems we will and have to deal with in the future)
- Individual and society
- Sickness and health
- Technology and society
- Fundamentals of existence.
Societal challenges play a different role for different people in different countries.
Sustainability: meet the needs of the present without compromising the ability of future
generations to meet their own needs (UN 1992). Maar wat zijn deze ‘needs’ en zijn die
hetzelfde voor personen in de toekomst en wanneer is ‘the future’?
- How does this relate to economics --> intertemporal choice. We are talking about
generations. Individuum: consume today vs. consume tomorrow. Generations: old
generation consumes today vs. young generation consumes tomorrow.
- --> choice with social effects: do future generations enter the today’s decisionmaker’s utility function? Other regarding preferences/social preferences.
Intergenerational/dynastic altruism – bequest models
o What are the needs: welfare, but how to define it and what is the value of
welfare today and tomorrow?
o Who decides? Consumers, companies, governments (microec. Theory of
the firm pol. economy)
The sustainable development goals: “The 2030 Agenda for Sustainable Development,
adopted by all United nations member states in 2015, provides a shared blueprint for peace
and prosperity for people and the planet, now and into the future. At its heart are the 17
Sustainable Development Goals (SDGs), which are an urgent call for action by all countries developed and developing - in a global partnership. They recognize that ending poverty and
other deprivations must go hand-in-hand with strategies that improve health and education,
reduce inequality, and spur economic growth – all while tackling climate change and working
to preserve our oceans and forests.
History of SDGs:
• 1992 - Earth Summit in Rio de Janeiro, 178 countries adopted Agenda 21 (plan for
global partnership to improve human lives and protect the environment).
• 2000 - Millennium Declaration at UN Headquarters in New York  Millennium
Development Goals (MDGs) to reduce extreme poverty by 2015.
• 2012 – United Nations Conference; members decided, inter alia, to launch a process
to develop a set of SDGs.
• 2015 – Members agreed on the 2030 Agenda with 17 SDGs.
The underlying framework is the BSE framework.
- Biosphere: 6 (clean water & sanitation --> Ensure availability and sustainable
management of water and sanitation for all), 13 (climate action --> Take urgent
action to combat climate change and its impacts), 14 (life below water -->
Conserve and sustainably use the oceans, seas and marine resources for
sustainable development), and 15 (life on land --> Protect, restore and promote
sustainable use of terrestrial ecosystems, sustainably manage forests, combat
desertification, and halt and reverse land degradation and halt biodiversity loss)
- Society: 1 (no poverty --> end poverty in all its forms everywhere), 2 (Zero hunger
--> end hunger, achieve food security and improved nutrition and promote
sustainable agriculture), 3 (Good health and well-being --> ensure healthy lives
and promote well-being for all at all ages), 4 (quality education --> ensure
inclusive and equitable quality education and promote lifelong learning
opportunities for all), 5 (gender equality --> achieve gender equality and
empower all women and girls), 7 (affordable & clean energy --> ensure access to
affordable, reliable, sustainable and modern energy for all), 11 (sustainable cities
and communities --> make cities and human settlements inclusive, safe, resilient,
and sustainable), 16 (peace, justice and strong institutions --> Promote peaceful
and inclusive societies for sustainable development, provide access to justice for
all and build effective, accountable and inclusive institutions at all levels).
- Economy: 8 (decent work and economic growth --> Promote sustained, inclusive
and sustainable economic growth, full and productive employment and decent
work for all), 9 (Industry, Innovation and Infrastructure --> Build resilient
infrastructure, promote inclusive and sustainable industrialization and foster
innovation), 10 (reduced inequalities --> reduce inequality within and among
countries), 12 (responsible consumption and production --> ensure sustainable
consumption and production patterns).
SDG indicators:
- Targets & indicators --> to measure if the SDG is achieved or not. Target = general
goal you want to reach until 2030. Indicators = how you can measure it and to
have a particular level of the indicators.
Critique on SDGs:
- The goals do not go far enough or show little progress.
- Goals ignore underlying inequality in international systems (conflict of goals
across countries).
- Goals are top-down ignoring the local context of different countries).
-
Goals are rather a wish list (not binding, unclear strategy)
Lack of data (different countries have different systems for data)
Still growth-addicted
Systematic reasons not addressed (trade agreements
Measurement issues
Nevertheless, SDGs are one way to tackle societal challenges.
Greenwashing refers to marketing strategies designed to make a company and/or its
products appear eco-friendly or sustainable despite such claims being exaggerated or even
fraudulent.

How does economics address societal challenges?
• The "JEL" classification system originated with the Journal of Economic Literature and
is a standard method of classifying scholarly literature in the field of economics. It is
used in many of the AEA's published research materials.
Specialization related (ACC): sustainable accounting --> the practice of measuring, analyzing,
and reporting a company’s social and environmental impact. (measure, disclose, and add
credibility). (SDG 13 and 16 are important).
We should be able to give 2 examples of how your specializations address the sustainable
development goals. (denk hierbij aan de huiswerk opdracht). How do particular papers deal
with SDGs? To see how your discipline is dealing with SDGs.
Lecture 4 – What is digital transformation? (wed 13 sept)
These concepts are not the same!
Digitization: transition from analog to digital
Digitalization: Improve business processes by leveraging digital technologies
Digital transformation: leverage emerging technologies to build new business systems,
business models, and consumer & employee experiences.
Some key technologies:
 big data --> large size, high dimension, complex structure, real-time. The 5Vs of Big
data are Volume (amount of data), velocity (rate of data received, how quickly),
variety (different types of data --> structured, semistructured and unstructured),
veracity (quality of data), value (analytics on the data, makings sense of data
otherwise data is useless).
o Some examples: traffic control, risk management, crime detection,
meteorology, healthcare, neuroscience
 machine learning --> it’s a subset of Ai that includes techniques that enable machines
to improve at tasks with experience. It includes deep learning. It’s an algorithm that
learns the data, build a prediction model and classifies new data that comes in.
Machine learning models improve over time as they are exposed to more data,
leading to more accurate outcomes and predictions. Examples are: siri (VPA), uber,
social media (personalized adds)



o There are three types of machine learning:
 Unsupervised learning: way to cluster unlabeled data sets.
 Supervised learning: trained on a labeled data set.
 Reinforcement learning: maximisation of rewards in dynamic
environments.
distributed ledger technology --> blockchain technology to enable the secure
functioning of a decentralized digital database to eliminate the need for a cnetral
authority to keep a check against manipulation. Distributed ledger technology (DLT)
refers specifically to the technological infrastructure and protocols that allow the
simultaneous access, validation and updating of
records that characterizes distributed ledgers. It
works on a computer network spread over
multiple entities or locations.
o Properties of DTL = distributed,
anonymous, time-stamped, unanimous,
immutable, secure, and programmable
o Examples: bitcoin (blockchain), smart
contracts, secure transactions.
internet of things --> global network of machines and devices that can interact with
each other. It’s a general term used for objects interconnected through networks,
that encompass processing and sensor capabilities, allowing the devices to transmit
recorded information from the outside environment. (interconnected nature of
devices).
o Examples: Iot devices, IoT big data, IoT dashboard, industry IoT, IoT
Healthcare. (control (a system of) devices from elsewhere).
cloud computing --> A model for enabling convenient, on-demand network access to
a shared pool of configurable computing resources (for example, networks, servers,
storage, applications, and services) that can be rapidly provisioned and released with
minimal management effort or service-provider interaction. This is the NIST
definition. Cloud computing is a way to organize big data from different locations all
over the world by different people.
o 3 fundamental models: (they have
different threats and possibilities, see
pic.)
 Infrastructure as a service
(IAAS): Apply own platforms and
applications on rented server
space from everywhere. Own
maintenance.
 Platform as a service (PAAS):
More pre-defined tools to
develop platforms and applications. Provider is responsible for system
and management.
 Software as a service (SAAS): Software and applications are provided
but can be accessed from everywhere.
o Advantages: Scalability (pay only for how much use; easy to scale down and
up), Outsource server storage, Higher Data Security (compared to own
implementation), Robust recovery measures, Better maintenance due to
synergies.
Artificial intelligence (AI): any technique that enables computes to mimic human
intellignece. It includes machine learning. It’s the broadest term used to classify machines
that mimic human intelligence. It is used to predict, automate, and optimize tasks that
humans have historically done, such as speech and facial recognition, decision making, and
translation.
– Weak AI: Applied to a participant situation, AI that is trained for a particular
task
– Strong AI: Self-consciousness to some extend.
Challenges big data/algorithms:
Digital transformation and Economics
We have different sorts of goods: digital goods (production costs is high at the beginning),
network goods, electronic markets & market design, digital economics.
Digital goods: Intangible goods, Digital goods refers to any goods that are sold, delivered and
transferred in digital form. Features are:
– No physical body, no physical erosion (the good itself doesn’t change), high
production cost first time, negligible variable costs, easy to copy.
– Producers determine scarcity and access, product differentiation, price
differentiation, low costs, protection through copyright and intellectual
property rights.
– MC=MR is=0 is not sufficient for making production decisions
Disruptive technologies: Innovations that come to replace a process, a product, or
technology that is already well-established:
– Video streaming/rental shop
- Uber/Caps
– AirBnB/Hotel
- Music Streaming/Records
– Whatsapp/SMS
- Wikipedia/Encyclopedia
– Cryptocurrency/Currencies
- Online Shops/Retail
Network goods: Network effects arise where current users of a good gain when additional
users adopt it. Examples: Telephone, Fax, Social Networks, Whatsapp, Chamber of
commerce, Trade Union
• Network Economics: High investment to generate critical mass (sometimes with
providing the good/service for free), afterwards the structure allows for subsequent
growth, most networks cannot grow indefinitely but become either congested
(overuse due to system capacity) or saturated. Switching costs might be high due to
the login effect.
– High investment to generate critical mass (net als bij digital goods)
– Structure allows for subsequent growth
– Networks become either congested or saturated (they were the one that
made it)
– Switching costs might be high due to the login effect
Electronic market: examples of market design are matching markets (What is the best
algorithm to match supply and demand? --> TINDER), auction theory (what is the best
pricing/allocation mechanism?), pricing strategies (pricie discrimination --> spotify
goedkoper voor studenten), and reputation mechanism (how to improve credibility of the
system? --> Hotels).
Digital economics: how do standard economic models change when considering digital
technology? REDUCTION OF COSTS.
- Lower search costs: compare prices & quality, digital information flow
- Lower replication costs: digital goods
- Lower transportation costs: digital goods
- Lower tracking costs: personalisation, price discrimination
- Lower verification costs: reputation, DTL.
How does Digital transformation address societal challenges?
Big data:
• Monitor prices in real time
• Pandemic Mobile phone tracking (SDG 3)
• Citizen reporting assessment
• Resilience of spending patters
• Postal traffic as indication for trade & growth
• Satellite Imagery combined with citizen observations
• Sentiment analysis to understand public
opinion
Machine learning:
DTL:
IoT:
Cloud computing:
Example: big data addresses SDG3 by detecting infection chains. In this case, big data is the
location data from mobile phones of the population that allows tracking peoples past
locations. (be concrete and provide a clear example).
Lecture 5 – A1 Sustainability accounting and reporting (mo 18 sept)
Accounting is an active agent in a dynamic and evolving social world. We focus on the Grand
Societal challenges of our times. Our goal is to identify actionable outcomes (accounting+).
Alliander is a group of companies. Their new energy markets offer innovative and sustainable
solutions and services that contribute to the new energy system.
Sustainabiltiy accounting: gathering of sustainability-related
information as basis for decision making.
Sustainability management control: use of management tools to
influence sustainability-related organizational behavior.
Sustainability reporting: disclosure of sustainability-related
ifnroamtion to internal and external stakeholders.
Ideal outcome of continious process of accountability: improved sustainability (--> SDGs)
performance over tiem.
- Sometimes there are alternative differentiations in the literature:
o 1 + 2 with internal focus classified as “sustainability management
accounting”
o 3 with an external focus classified as “sustainability reporting”.
Sustainability-related information is relevant on various levels:
- Product level: help inform customers about sustainable choices, foster sustainable
consumer behavior, or help product designers improving sustainability
performance.
- Process level: help companies include sustanability considerations into daily
business.
- Organizational level: can support sustainability management at the company level
and beyond.
Many elements of sustainability management depend on reliable information -->
sustainability accounting is a vast topic (unusually large in size/ extent).
- In this course: we focus on exemplary tools and areas of application (life cycle
assesment, carbon accounting etc.)
Life cycle sustainability assessment (LCSA): it’s a tool for information gathering in
sustainability accounting. It’s about collecting and assessing information about
environmental, economic, and social resources used during the life span of a product.
- Product life cycle = physicial life cycle describing all stages through which a
product passes. Often starts with mining and extraction of raw materials, then
design and production processes, shipping and transportation, use process until
the end of the product life and corresponding disposal or reuse or recycling of the
products or materials.
- Large scope of LCSA --> nearly impossible to truly assess entire life cycle of a
product.
Different elements of LCSA: (see dia 16)
- Environmental life cycle assessment (ELCA OF LCA) --> covers ecological aspects,
most common element. = ecological aspects
- Life cycle costing (LCC) --> helps evaluating cost occuring in the life cycle (e.g.
production, transportation, consumer consts or costs for disposal). = economic
aspects
- Social life cycle assessment (SLCA) --> analysis of social and socioeconomic
impacts. Much shorter history compared to ELCA, partly because assessment of
social impact often more complex than assessment of environmental impact
factors. = social aspects.
- ALL can be used in combination or independently.
Carbon accounting:
- Greenhouse gas emissions of central concern for sustainability maangement
-
Companies around the world define targets for themselves to reduce GHG
emissions.
To be able to set targets, relevant information need to be available --> Carbon
accounting as an own area of sustainabiliy accounting
Tools and procedures of CA focus on GHG in gneeral
o Typical 1st question in CA: where did emissions take place? --> the answer
is relevant to
determine
responsibilities,
identify main emission
sources and levers to
reduce emission, and
avoid missing relevant
emissions as well as
double counting.
o Common distinction in
CA between scope 1, 2
and 3 emissions.
Challenges in CA:
- Scope 1 emissions are relatively easy to measure.
o Adequate accounting information systems collecting and assessing
relevant data necessary to be able to calculate emission
o Setting up and maintaining such systems in itself is substantial task but can
be done largely independent of other actors
- Data on scope 2 emissions often relatively easy to obtain --> they mainly cover
GHG emissions from energy production, which is a well-researched field
- Calculating scope 3 emissions requires extensive and often complex data from
actors up- or downstream in supply chain.
o Obtaining data often difficult or prone to uncertainties or inaccuracies
o Information on scope 3 emissions are often very informative
Management systems for sustainability (environmental management system)
• Provide procedures of how to implement certain aspects of management into
strategy and daily business
• Coordinate and systemize organizational activities by using defined and documented
control and feedback mechanisms
• Not restricted to sustainability issues (classic example: quality management systems)
• Procedures and details are outlined in certain management system standards
• Standards are certifiable à auditors document organizations compliance and issue
certificate to document compliance
• However, companies can also set up management systems without external audits
and certifications
Management control systems are related to sustainability control systems:
- Strategic planning --> sustainability planning
- Budgeting --> environmental budgeting, sustainability budgeting
-
Hybrid measurement systems --> sustainability performance measurement,
sustainability balanced scorecard.
Balanced scorecard as tool in strategic planning at business unit level:
- Strategic planning and management performance measurement system was
introduced by Kaplan and Norton.
- The BSC incorporates 4 perspectives: financial, customer/market, ST efficiency
(internal process) and LT learning and development factors. the idea is to ling LT
strategic objectives with ST actions.
- Each perspective contains 4 kinds of information
o Objectives (high level organizational goals)
o Measures (how progress for the repsective objective is measured)
o Targets (specifiec target valeus for each measure)
o Initiatives (action programs developed to achieve objects)
- The perspectives should be integrated and linked via cause and effect.
The sustainability BSC --> basic idea is to include social and environmental issues in the
existing BSC to produce a SBSC. 3 options to do this:
1. integrating social and environmental measures withing the existing 4 quadrants
(perspectives).
2. developing a separate, but linked, sustainability scorecard
3. adding non-market elements to the scorecard.
Sustainability reporting: is a term commonly used to describe a range of practices where
organisations provide information on sustainability matters. It provides and substantiates
information about the status and progress of corporate sustainability towards internal and
external stakeholders through formalized means of communication.
- The majority of large multinational companies but also many smaller companies
regularly publish sustaianbility-related report. There are various terms used
interchangeable (CSR report, sustainability report, non-financial report etc). it
ususally covers multiple aspects and dimensions of sustainability (but a onedimensional report is also possible).
- Companies are not restricted to publishing annual or biannual reports but can
also be conducted during the fiscal year (press releases or on website).
Sustainability reporting concepts and terms:
- Integrated or combined reports: 3 sustainability dimensions (financial, ecological,
and social in one report).
- Specialized sustainability, CSR reports: 2 sustainabiltiy dimensions (ecological and
social; financial usually not covered)
- Isolated environmental or social reports: 1 sustainability dimension (ecological or
social).
There is a growth in non-financial reports and the assurance thereof.
Normative perspective: sustainability (nonfinancial) reporting helps investors, civil society
organisations, consumer, policy makers and other stakeholder to evaluate the non-financial
performance of large companies and encourages these companies to develop a responsible
approach to business (according to EU). This includes how they are addressing the SDGs
through their purpose and values, supply chain, and in the development of their talent.
The starting point is real impact effects! Real business activities (firms’ carbon emissions) are
portrayed through sustainability reporting and this in turn affects again real business
activities (decrease in firms’ carbon emissions). But what sustainability information do firms
need to report?
- For decades, there was no single dominating standard setting institution. Now, we
have the EU nonfinancial reporting directive and the EU taxonomy (mandatory
sphere).
- Firms are supposed to report material information but what is material?
o Impact materiality --> material topics are those that reflect the
organization’s most significant impacts on the economy, environment, and
people including impacts and human rights (stakeholder-oriented).
o Financial materiality --> information is financially material if omitting,
misstating, or obscuring it could reasonable be expected to influence
investment or lending decisions that users make on the basis of financial
performance and enterprise value (invester orientend).
o Impact materiality + financial materiality = double materiality!
Materiality assessments might differ depending on the framework/standards that firms
adhere to!!!!
Single materiality focus might further
increase existing (voluntary) reporting
trends.
- Financial materiality: ISSB
(Further consolidation)
- Double materiality: NFRD,
CSRD (further regulation).
There are steps takes towards
mandatory reporting for large compannes --> mandatory character for large public
companies headquartered in the EU --> there is however still little guidance on specifically
what and how to report --> there is also potential for discretion.
The ESRS (European sustainability reporting standards) draft was developed by the European
financial reporting advisory group (EFRAG). ISSB is global. It consists of 4 topics: cross-cutting
standards, environment, social and governance.
Lecture 6 – Sustainability challenges at Alliander N.V. (tue 19 sept)
Alliander:
- Biggest regional energy network operator of the netherlands
- All the shares in Alliander are directly or indirectly held by dutch provisional
authorities and municipalties
- Operation of 95.000km electricity cables in the ground, 42.000km gaspipes.
- 8000 employees
Hoe ze georganiseerd zijn:
Qirion: high voltage. Does a lot
of maintenance work for that
Kenter: metering energy
things
Firan: helps with natural
heating sources.
Alliander telecom: operational
technical infrastructure.
Their organization model is a wheel with pillars to solve the sustainble challenges. 2 nd and
3th line of defence.
- Finance directs to CFO.
- Internal audit directs to CEO.
Alliander has the Highest reliability of electricity (99.9998%).
Target 2030 alliander: not only making the energy transition possible but also helping to
accelerate it. It must also be affordable and a reliable energy system.
The SDGs waar ze voor streven zijn 7,8,9,11,12,13.
Transparency is important to enable the energy grid of the future (reporting). This is all in the
integrated report of Alliander. Ze vinden het belangrijk om een integrated report te hebben.
- Their reporting processes are based on PDCA.
- Assurance: 3 lines model and external assurance
Overarching question:
• How could management information systems (MIS) help Alliander make wellinformed ESG decisions? (value of the whole organization)
Concrete subquestions:
1. Which management information system(s) (MIS) would you suggest using to measure
and manage ESG information at Alliander, and why?
2. What data points and KPIs (Key Performance Indicators) would you suggest
integrating into the ESG-related MIS? Can you counter reliability concerns for those
data points and KPIs?
3. What are the organizational challenges (e.g., coordination, efficiently collecting data)
in successfully implementing such a MIS at Alliander?
Deze vragen moet je beantwoorden bij de elevator pitch. Use your creativity. (challenges:
data points, reporting parts). Liefste of focussen op de main question of op de sub questions.
Monday: elevator pitch training.
Lecture 7 - mo
ESG (environmental, social and governance)
Environmental:
- GHG emissions
- Carbon emissions --> scope 1,2, and 3.
- Energy use & renewable energy
- Waste
- Biodiversity
Social:
-
Human rights
Employees
Diversity
Equality
Human rights
Ethical business practices
Donations
Governance:
- Renumeration
- Executive compensation (fixed component and variable component, which are
dependent on ESG scores).
- Bribery (omkoping)
- Board composition. --> different types of diversity (men/women, nationality, age,
religion, culture) and expertise and independence of members.
- Accountability
- Transparent disclosure (greenwashing)
- Stakeholder engagement
- Risk management / compliance (dat je de wet niet overtreedt enzo)
- Shareholder (beschermingconstructies)
- Ethical behavior (quote of conduct)
Alliander gebruikt excel spreadsheets om dingen te reporten. Ze willen 1 integrated
comprehensive system. Maar ze willen alle ESG pillars ook combineren en dat is lastig.
Materiality --> is een maatstaf in auditing en accounting die bepaalt of een bepaald bedrag al
dan niet significant is. Bij een audit van de jaarrekening wordt gekeken of deze aan de
geldende regels beantwoordt en geen materiële fouten bevat.
Example: Philips, they have panel sessions with stakeholders to know what information is
important (ESG information), so then the company knows where they should report about.
Then the next question is: how do they measure everything (Water usage for example)? and
how to audit the information? So there are a lot of problems. (first sub question)
First subquestion: how to measure this information and how to audit it (For the auditor)?
Alliander maakt al gebruik van de CSRD regels van de EU. Zij willen al reasonable assurance
(omdat ze een voorloper zijn in NL).
- Zij hebben dus voor alleen de hele belangrjike key performances reasonable
assurance en voor de rest limited assurance.
2nd sub question: using panel sessions
They want to get rid of the excel systems but they don’t know how to do this.
3rd sub question: it’s really costly. Another problems: comparability is low and weak because
each company has another MIS. There is no benchmark.
Problem BSC: causal relationship between these dimensions is hard to find.
Another problem: they are still looking for real information experts (creative thinkers),
because they’re lacking.
lines of defence model
1st line: information system
2nd line: the fin. Controller, compliance manager,
risk managers. They oversee and try to improve
the first line (first filter/check of mistakes)
3rd line: Erik Hessels, they should be completely
independent of the first 2.
4th line: external auditer, audits whether the
internal audits works effectively (controleert weer de 3rd line
5th line: regulator (authority fin. Markets)
Lecture 8 – tue (elevator pitches for alliander)
Alleen pitches. Erik van Hessels (van Alliander) heeft niks meer gezegd.
Lecture 9 – introduction to accounting and digitalization (mo)
Building a data driven culture is hard. To capture what it takes to succeed, the authors look at
the first two years of a new data program at Kuwait’s Gulf Bank in which they worked to
build a culture that embraced data, and offer a few lessons. First, it is important to start
building the new culture from day one, even as doing so is not the primary mandate. Second,
to change a culture, you need to get everyone involved. Third, give data quality strong
consideration as the place to start. Finally, building this new culture takes courage and
persistence. (article)
These 2 weeks: digital transformation in accounting, again with Alliander
It’s first digitization, then digitalization and then digital transformation.
Er is een nijpend tekort aan accountants (door vergrijzing).
Digitalization is expected to transform accounting --> there is a need for increased efficiency
and effectiveness:
- Practioners are being asked to do more with the same or fewer resources
- Increased online collaboration requires new digital tools
 Accounting is a great place to start:
 No need to get ‘buy-in’ from others outside the area
 A low-risk opportunity to try out new technology before rolling it out to other
areas
 Improve efficiency, accuracy (e.g., measuring, reporting, forecasting) and
profitability
Whatdo we expect from digitalization in accounting (Lawson 2020 paper)?
- Efficient transaction processing and reporting
- Getting the right information to the right people in a timely fasion
- Combining accounting expertise with analytics and companywide data to make
better business decisions
- Obtaining new information from unstructured data sources
- Improving data security
- Increased information content of data used for enterprise risk assessment
- Improving accurcary of forecasts.
The volume of data has increased dramatically. According to Deloitte, 90% of all data have
been created over the past 2 years (said in 2018 by ICAEW). They say that we are at the
beginning of a new industrial revolution in which technologies such as AI, machine learning,
internet of Things, blockchain technology are really changing our world and challenging the
profession of accountancy (Marr, 2018, ICAEW).
How is it related to digitalization for accounting?
- Drones (invetory counting)
- XBRL (extensible business reporting language)
- Automation, robotic process automation (data processing)
- Natural language processing, chatbots (ChatGPT)
- Use of social media for reporting purposes
- Big data and Analytics
- AI and machine learning (in auditing)
- Blockchain (cryptocurrency, NFTs, smart contracts, auditing purposes)
- Internet of Things (IOT), cloud-based services
XBRL stands for extensible business reporting language. It’s a freely available global
framework of accounting standards used for exchanging business information. It’s based on
XML coding and is a standardized way of transmitting financial records around the world.
iXBRL, where I stands for inline is an update that allows for XBRL metadata to be embedded
in an HTML document. XBRL/ iXBRL is mandatory for public companies in the US (SEC) and
the EU. XBRL is a structured way of disgital reportig!!
Social media: a potentially interesting setting:
-
Social media allow for increased interaction between management and
stakeholders
It allows stakehodlers to voice their opinions about firms more easily
There is an increased interaction between stakeholders among themselves, this
may pose a threat for firms’ reputation.
It makes corporate information more accessible to a larger and more diverse
public.
It has other formal characteristics (length, tone, style)
Thus, what we know about traditional reporting settings may not generalize to a social media
setting. (shell video)
And example of data and analytics is the ERP databases. These contain broad and disparate
data sets, which may include sales performance statistircs, consumer reviews etc. Machine
learning algorithms can be used to find correlation and patterns in such data. Those insights
can then be used to inform virtually every area of the business including optimizing the
workflows of IoT devices within the network or the best ways to automate repetitive or
error-prone takss.
AI = the theory and development of computer systems able to
perform tasks normally requiring human intellingence, such as
visual perception, speech recognition, decision-making, and
translation between languages
Ai and accounting: there is a growing area of enterprise
machine learning application (see video of AI and machine
learning in auditing).
But you have to think about the role of accountability and
responsibility when using AI (Ethical concerns --> good or bad
intentions. AI doesn’t necessarily share our values, which may result in biases.
Generally, AI is used as a tool for pattern recognition and used as a tool for classification
(pattern formation). AI needs as input: large amounts of repetitive data, and a directed
analysis (a question, goal, objective)
Current state of digitalization for accounting:
- Survey with 308 members of Dutch organization for registered controllers. They
agree that the following phenomena are taking place in their organizations and
expect an increase in the next 5 years:
o Digitalization of the primary processes
o Digitalization of the administrative and/or financial processes
o Digitalization of the business model
o Collecting/availability of data to support decision-making.
- Further, the following technological development are already used by many
respondents and considered valuable by many others: Robotic process
-
automation, big data, AI, ML, innovative payment systems, IoT, cloud-based
services, XBRL.
Other technological developments are not currently being used or not considered
valuable: Quickbooks (automation of fin. Data processing and organization),
Drones, regression analysis, Alteryx (enables analysis of business data), Python,
Stata, R software, Cryptocurrency, other blockchain technolohies, smart contracts,
databox, augmented virutal reality, and metaverse.
Data-driven decision-making: respondents indicate to participate in the following activities:
- Collecting quantitative data
- Assessing the quality of data
- Combining different datasets
- Data visualisation
- Determining which analyses are necessary to support decision-making
- Critically interpreting statistical output
- Using analyses to support decision-making
They also argue that they have enough skills to perform these activities. At the same time,
respondents see room for improvement for each of these activities.
Interestingly, some activities are considered valuable for their function but in which
respondents are not participating and for which they would need to improve their skills:
- Data wrangling and cleaning (transforming and preparing data for analysis)
- Statistical data analysis
- Data mining (discovering relations in large datasets)
- Process mining (using data to analyse business processes)
- Predictive modeling
- Ensuring data security
Overall, respondents don’t program, don’t think it’s valauble for their function and do not
feel skilled.
Specifically related to working with data, accounting practitioners use the following tools:
- Dataprocessing (Excel)
- Enterprise resource planning (SAP ERP, or other ERP software)
- Data visualisation (Power BI, Tableau, other visualisation software)
- Managing relational databases (oracle, other software).
Barriers of digitalization for accounting:
- Lack of resources (time, money, knowledge, (IT) people)
- IT infrastructure (concenrs about data quality and reliability, fragmetned data
systems, outdated systems)
- Mindset (resistance, lack of support from the top, missing sense of urgency,
bureaucracy).
What does it mean for Us Accountants: job demands becomes data-savvy and reconsider
human decision-making, responsibility and accountability of the accounting and controller
(Human in the loop), reduced role for traditional doubly-entry information, and Psychological
barriers (understanding the black box, algorithm aversion).
So, where to start:
 Starting simple and small when first implementing projects.
 Expanding the sources of data used and exploring potential uses not only of data
available internally but also of data available externally.
 Getting information based on data into the hands of those who need it on a real-time
basis.
 Getting upper management on board (or in the case of cheap tech, start a bootstrap
movement).
 Getting IT and finance organization buy-in.
 Forming a cross-functional team and communicating well.
 Adequately evaluating the technology and potential vendors.
 Building strong data governance and quality infrastructure in order to ensure data
integrity and quality.
 Most important for accounting practitioners: Understand technology, not necessarily
being able to implement it.
 e.g., blockchain
Lecture 10 – Digital Challenges at Alliander
Toeslagen affaire en digitalization: door het programma wat ze gebruikte, werden veel
ouders benadeeld. Dit is een voorbeeld.
Digitalization at Alliander: digging a hole, they have to rely on data. How does digitalization
works within alliander. Data and AI are solutions to handle the problems. The whole energy
market is chaning now.
Alliander is an asset company: an asset is a cable for example. Energy transition and
manufacturing puts pressure on Liander’s mission. A reliable, affordable, and accessible
energy supply. (percentage = 99.998%).
Goals (strategy) of the last 2 weeks: (focus points)
1. Building nets: Getting more cables in the ground
2. Flexibileze (distribute between everyone and do it in a smart way, wanneer je de
power gebruikt --> tomatenkwekerij, wanneer er genoeg energy is) congestion
between supply and demand). Maximize the usage of the grid (zodat bedrijven niet
hoeven te wachten omdat het energy grid is full
3. Communicate more. Explain when Alliander cannot give reliable energy.
Communicate about success and when he can rely on reliable energy because that’s
when he can do his investments.
System model: costumer value. 4 types of customers:
- Government
- customer
- Society (increase digitaliztion option in the future)
- Market (bring the data together)
They have 8 customer products now.
at is een regionale energiestrategie? In een regionale energiestrategie (RES) onderzoekt
een regionaal samenwerkingsverband van provincies, gemeenten en waterschappen de
mogelijkheden voor de uitvoering van de energietransitie.
Digitalization = Solution in the big problem they face as grid operator for now and in the
future.
- Digitalization is all about the power of imagination, but how do we facilitate this?
Picture: idea of topics Alliander is working on (gaat over digitalization). You always have to
take into account privacy when working on digitalization.
Each of the steps in the value chain, digitalization plays a role --> this is done by AI.
Facilitating the digitalization ambitions of the value chain by bulding the AI capability (denk
aan voorbeeld van de bakker). Many different aspects where digitalization can help. These
can be improved with AI!! Daarmee is Alliander nu aan het experimenteren (robots, drones)
Accountant perspective: conservatief. Waarom moet de accounting profession be aware of AI
and focus on it? If we only rely on algorithms without being sure enough that is does what it
has to do, there could ontstaan problems (toeslagen affaire). It’s important to manage
expectations! Could lead to reputational damage of the board.
Analytics within alliander is more than just AI and ML. it’s divided into 4 parts:
- ML and statistics
- Physical modelling
- Graph theory & operations (simulate the grid as a graph)
- Optimization (optimal allocation of resources and material f.e.)
Core competency challenges:
- Correlations vs. non-sense correlations
- Delivering business value
- Side effects (denk aan arme wijk in stad)
Dangers of AI:
- Ethical discrimination problems
- Super intelligence, robots takes over the world (hij is hier nog niet bang voor)
Main question:
• How could accounting help Alliander to make well informed decisions in artificial
intelligence driven business processes?
Subquestions:
• To which risks does Alliander expose her customers, when business processes will be
digitalized by artificial intelligence?
• Based on the identification of risks, how does a CTO make a good and valid decision
about the preferred level of AI use?
• How does the CFO judge the reliability of AI produced decisions? (reputation damage
for example)
De vragen builts up. Eerste vraag hebben we het al over gehad in de lecture. Second
question: where do they base the decision on? 3rd question: looking at finances. Good
source to start: annual report of Alliander. (risk paragraph in the annual report).
Other sources: video on youtube about Alliander and digitalization, AI at alliander is at the
website of Alliander.
CTO = chief transformational officer
Lecture 11, Lecture 12 no notes
Lecture 5 – All students again (mo 16 okt)
Outline week 42: Methodology, take-aways from guest lectures, reflection of week 38-41,
course feedback, exam and online evaluation.
Methodology: de studie van de wetenschappelijke methoden, de procedures en werkwijzen,
die moeten worden gebruikt om kennis te verwerven en om de wetenschap vooruit te
helpen. welke methodes je gebruikt hebt om tot je resultaten van je onderzoek te komen
- Deductive reasoning: theory --> hypothesis --> observation (data) -->
confirmation (analysis). This is a normative approach; make assumptions and form
anmodel, use it to form testable hypotheses that can be empirically tested. (from
general to particular). Models can be tested an refined by considering data
analysis. = Deduction
- Inductive reasoning: observation (data) --> pattern (analysis) --> hypothesis -->
theory. Collect empirical data to generate hypotheses and form a theroy… you go
from the part to the whole, from specific to general, from narrow to wider…
generalisation. = Induction. Example = case study.
Components of the research process are: Theory (ideas on how a system works or how
relationship (can) look like), hypothesis (testable relationship derived from theory), data, and
analysis (to see whether the data conforms the theroy.
Other-regarding preferences are preferences that attach value to the well-being of others as
ends in themselves (other humans, species or nature as a whole). (dictator game, split cake?)
Drawing general conclusions from data requires induction.
- But general conclusion cannot truthfully be drawn from induction. The next
observation can show some different result. Think about the white swans. It might
be true locally but not globally (background theory is that the location matters)
- Falsification: researchers must be able to falsify a theory, otherwise it is not a
scientific theory (Popper). Needs to take the background theory into account. If it
cannot be falsified, it’s not a scientific question (like god exists). All scientific
hypotheses must be falsifiable in the natural world (according to Poppwer).
Models: some people say that all models are wrong, meaning that it will never represent the
exact real behavior. Having said that, even if a model cannot describe exactly the reality it
could be very helpful if it is close enough. For models: perspectives and context matters. A
one-to-one map is not possible (het is nooit helemaal hetzelfde). Another quote: manymodel thinkers make better decisions.
- Goal of models: to reduce the complexity (theory of complexity)
Positive --> What is! --> objective reality (biological categorisation), it describes the world.
It’s true or false. = Anna (Peter) is a female (male).
Normative --> What ought to be --> subjective opinion, tells about the views of the world
(values, tastes), cannot be proven true or false. = Anna (Peter) is the toughest woman (man).
These are positive vs. normative statements.
Economic models: explain economic phenomena in an abstract way. It’s verbal, graphical and
mathematical
Testing economic theories: able to quantify involved variables and see whether theoretical
prediction from the model holds.
A good theory: makes claims which can be tested open to falsification.
Shiller (narrative economics book) argues that studying popular stories that affect individual
and collective economic behavior—what he calls “narrative economics”—has the potential
to vastly improve our ability to predict, prepare for, and lessen the damage of financial crises,
recessions, depressions, and other major economic events.
Methods used in economic decision-making: Mathematical economics (like optimisation
micro 101, equilibrium analysis, comparative statistics, dynamics), Decision analysis ( set of
alternatives, chance/unknown events that effect the outcome, outcome --> solution concept,
example = decision tree with expected values), Game theory (set of alternatives, multiple
players, payoff mapping of decision --> solution concept, example = prisoner’s dilemma.
Nash equilibrium = no one wants to change, with a tree, make use of backward induction)
So economists’ toolbox to reduce complexity is: Mathematics (optimization, intertemporal
optimization differential equations), decision analysis / game theory, econometrics
(understanding the world in terms of data analysis), experimental economics/finance
(understanding the world in terms of running experiments to test market design and
economic behavior), simulations and surverys.
Methods of empirical anaylsis: time series, panel data, multilevel, categorical data,
qualitative methods, treatment effects & endogeneity, experimental methods.
Methods that are less frequently used in economics: ANOVA (Analysis of Variance --> oneway and two-way). It tests 3 or more groups (factors) for mean differences on a continious
response variable. ANOVA is a special case of regression), independent component analysis,
factor analysis (have a big data set, find clusters (or predefine clusters) and figure out the
relationship between these clusters. These last 2 are often used for questionnaires in
personality research), System dynamics, Bibliometric analysis (statistical analysis of text &
meta data, make distributions, clusters or networks of such content, reference analysis)
SDG relatated: Systematic literature review --> reporting items for systematic reviews and
meta-analysis (PRISMA) --> checklist. But the relevance of the research is important
(Example: Noise traders)
A flaw of research is that it may have so little impact.
Digital Key technologies and Economics
Digitalisation of the brain = decision neuroscience (Neuroeconomics)
- Dealing with big data
- Behavioural data vs. brain reaction
- Think of Alan’s talk about neuroscience.
Cloud computing: access to data, sharing code, working on same documents, data storage.
Allow researchers to gather data for meta analysis and verification of results and source of
data. There is even a new discipline, called cloud economics: the study of cloud computing’s
costs and benefits and the economic principles that underpin them.
- Cloud computing allows for a higher chance of reproducibility when using fair
designed data sets. The goal is to have better tools to allow for a reproduction of
the results.
Big data and machine learning. We use this for forecasting and predictions. We use big data
to increase the accuracy. Use sentiment analysis / natural language processing (textual,
audio, visual data to detect communication patterns / effects that influence markets). Use
Image processing and computer vision (satellite data to measure economic activity (or
growth)). And use it to process automation and optimization to increase the efficiency. We
use machine learning techniques to analyse big data
Threats of new technologies:
Satellite talks
Exam relevance:
What is the gig economy? Give an example.
What are transaction costs and how does the gig economy reduces those? Give an examples.
What is principal agent theory about and how might it be related to the gig economy? Give
an example.
Exam Relevance
Why is the economics cybersecurity relevant for sustainability and resilience of a system?
Wolter provided three application areas on the economics of cybersecurity.
- What is a relevant economic decision in ‘security decisions’?
- What are examples for externalities and information asymmetry issues that affect
cybersecurity related to coordination?
- What is the insurance dilemma insurance companies face when making choices to
pay ransom or the damage/recovery costs?
Exam relevance
How can decision neuroscience help to understand econ decision making? Give an example.
How can reading emotions help to understand econ decision making? Give an example.
Exam Relevance:
Why is biodiversity loss a problem? Give one or two examples
Why is biodiversity related to a stock market? Elaborate on this idea
What is a tipping point?
Lecture 6
Eerst Satellite talks gedaan, daarna homework met de papers. Daarna take away van de
groups, daarna exam vragen.
A note on the credibility of journals:
• Predatory Journals
– Perform little to no review
– Charge post-acceptance publication fees (incentives?)
– Rapid decision
– Open Access (low publication costs)
• Consequence
– Low quality/non-accurate research
– Articles may be cited incorrectly as authoritative
– Accrediting bodies /university administrators may wrongly regard those as
productivity
measures
Exam: 3 parts --> part one (same
for all, concent lectures 1-5 (30%)
and guest lectures (10%)), part 2
(content week 38 & 39 (30%)), part
3 (week 40 & 41 (30%)). Exam
relevance has been provided.
Example of questions -->