The Development of Decision Support Systems

The Romanian Academy (1866-)
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Source:
http://www.
dougengel
bart.org/co
ntent/view/
209/448
More at
http://www.dougengelbart.org/content/view/374/464/

Engelbart DC (1962 ) Augmenting Human
Intellect: A Conceptual Framework.
• “…By ‘augmenting human intellect’ we mean increasing
the capability of a man to approach a complex problem
situation, to gain comprehension to suit his particular
needs, and to derive solutions to problems.
• [………………………………………………………………….]
• We see the quickest gains emerging from (1) giving the
human the minute-by-minute a service from a digital
computer […] and (2) developing new methods of
thinking and working that allow the human to
capitalize the computer’s help.”
6/13/2020 F. G. Filip 3

Engelbart DC (1962 ) Augmenting Human
Intellect: A Conceptual Framework.
• “…By ‘augmenting human intellect’ we mean increasing the capability of a man to approach a
complex problem situation, to gain comprehension to suit his particular needs, and to derive
solutions to problems.
• [………………………………………………………………….]
• We see the quickest gains emerging from (1) giving the human the minute-by-minute a service
from a digital computer […] and (2) developing new methods of thinking and working that allow
the human to capitalize the computer’s help.”
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Original Program Announcement for
“The Mother of All Demons” (1968)
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Source:
http://codinginparadis
e.org/weblog/uploade
d_images/engelbart-
749273.jpg

DSS: Concepts and
Enabling I&C Technologies to
Augment Problem Solving
Capabilities of Decision-makers
F.G. Filip
The Romanian Academy,
Bucharest, Romania
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AGENDA
• The Computer and Automation [at large] as
Viewed Yesterday and Today
• Decisions and Decision-makers
• DSS a Class of Evolving Information Systems
• Essential Technologies
• Conclusions
• References
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AGENDA
• The Computer and Automation as Viewed
Yesterday and Today
– Automation –An Evolving Concept and Domain
– The Computer in The Private life
• Conclusions
• References
6/13/2020 F. G. Filip 8

Umberto Eco
(1986)
Il computer non è una
macchina intelligente che
aiuta le persone stupide,
anzi è una macchina stupida
che funziona solo nelle mani
delle persone intelligenti”
(“The comuter is not an
intelligent machine that
helps the stupid people ,
but is a stupid tool that
functions only in the hands
of intelligent people“) .
Photo://www.napocanews.ro/
2016/02/a-murit-scriitorul-
umberto-eco.htmltorul-
umberto-eco.html
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P. Drucker (1909-2006)
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Source:
https://upload.wikimedia.org/wikipedia/commons/e/ea/Drucker5789.jpg
Peter Drucker (1967): “The
computer makes no decisions; it only carries
out orders. It’s a total moron, and therein lies
its strength. It forces us to think, to set the
criteria. The stupider the tool, the brighter the
master has to be—and this is the dumbest
tool we have ever had”

Peter Drucker’s Assumptions (1967):
The Age of Information
• “Assumption No. 1: Within the next ten years, information will
become very much cheaper. An hour of computer time today costs
several hundred dollars at a minimum; I have seen figures that put the
cost at about a dollar an hour in 1973 or so.
• Assumption No. 2: The present imbalance between the capacity to
compute and store information and the capacity to use it will be
remedied. We will spend more and more money on producing the things
that make a computer usable—the software, the programs, the
terminals, and so on.
• Assumption No. 3: The kindergarten stage is over. We’re past the
time when everybody was terribly impressed by the computer’s ability
to do two plus two in fractions of a nanosecond. We’re also past the
stage of trying to find work for the computer by putting all the
unimportant things on it—using it as a very expensive clerk.”
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Today: The Computer in The Private Life
“Almost everyone has a cradle-to-grave general personal assistant
application that builds up an integrated model of the person’s
preferences, abilities, interests, modes of learning, idiosyncratic use of
terms and expressions, experiences, goals, plans, beliefs” (Ex.
Oracle’s Siri; μSoft’s Cortana; Amazon’s Alexa, Google Assistant..).
• Predicted phenomena:
– weak telepathy: the AI understands what one has in mind and
why, completes that action […], and accomplishes actual goal ;
– weak immortality : PDA’s model , even after one’s death, is able
to continue to interact with family, friends, business associates,
carry on conversations, accomplish assigned tasks…;
– weak cloning: person’s instant duplication, ubiquity, multitasking
Source: Lenat 2017: “WWTS (what will Turing say)?”
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Operator 4.0 in Modern
Manufacturing
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(Source: Romero et al 2016)

Operator 4.0
• “Operator 4.0 : is a smart and skilled operator who performs not only
‘cooperative work’ with robots, but also ‘work aided’ by machines as
and if needed, by means of human cyber-physical systems, advanced
human-machine interaction technologies and adaptive automation
towards human-automation symbiosis work systems .
• Social Operator 4.0 is a type of Operator 4.0 that uses smart
wearable solutions together with advanced human-machine interaction
(HMI) technologies to cooperate with other social operators such as:
social machines and social software systems in order to communicate
and exchange information for mutual benefit and align/alter activities
as well as share resources so that more efficient results can be achieved
at the smart and social factory of Industry 4.0.”
• ( Source: Romero et al 2016)
6/13/2020 F. G. Filip 14

Social Factory
• “A Social Factory is a live enterprise social network
– with powerful middleware and analytics backend
– to improve the connection between social operators, social machines and social
software systems working together in a smart production environment, and
– the data created within the networking process towards a sustainable ‘learning
factory’.
• A Social Factory should be able to
– provide the right information and the right time and place (anywhere and any
time) to the right person, machine and/ or software,
– support humans under any working conditions,
– engage humans to contribute to new knowledge creation,
– treat humans, machines and software as equal partners
– learn from all this socialization of knowledge by explicitising it.”
( Source: Kassner et al 2017)
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SOURCE: https://www.coursera.org/instructor/andrewng
吳恩達

Andrew Yan-Tak Ng (吳恩達 )(2016):
“A lot of valuable work currently done by
humans:
– examining security video to detect suspicious
behaviors,
– deciding if a car is about to hit a pedestrian
– finding and eliminating abusive online posts
can be done in less than one second. These tasks
are ripe for automation”
6/13/2020 F. G. Filip 17

Automation, Division of Work,
Human Computer Collaboration
• Definition: “Automation is a process that controls a
function or task without human intervention.” ( Billings 1991)
• Remarks
– “Even highly automated systems need human beings for
supervision, adjustment, maintenance and improvement.” ( Bibby
et al 1975);
– “The hardest activities to automate with currently available
technologies are those that […] apply expertise to decision making,
planning, or creative work (18 percent).” ( Chui&Manika 2016)
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Automation Potential of Manual
Activities (WEF 2019)
Automatable
ocupations[%]
100 91 73 62 54 43 34 36 18 8 1
Automation
potential [%
of activities]
>0 >10 >20 >30 >40 >50 >60 >70 >80 >90 100
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Fewer than 5% of occupations consist of activities that are 100%
automatable with today’s technology, while 62% of occupations have at
least 30% of automatable tasks. Consequently, employees at all career
stages enjoy new tasks and responsibilities that demand the human skill
of dynamic decision-making in a changing environment. (Batra et al
2017)

Task Allocation Between People and
Automation Equipment
• Traditional Schemes:
– Comparison allocation based on MABA-MABA list: assigning each function
to the most capable agent (either people or machine),
– Leftover allocation: allocating to automation equipment every function that
can be automated, the rest remain to be performed by the people
– Economic design: the allocation scheme ensures economical efficiency.
• New Approaches:
– adaptive/shared automation: the control of functions dynamically shifts
between people and machines depending on environmental factors, operator
workload, and performance
– human centered automation, balanced automation, collaborative automation…
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Original MABA-MABA list of Fitts
• Humans appear to surpass
present-day machines in respect
to the following:
• Present-day machines appear to
surpass humans in respect to the
following:
Ability to detect a small amount of
visual or acoustic energy;
•Ability to perceive patterns of light or
sound;
•Ability to improvise and use flexible
procedures;
•Ability to store very large amounts of
information for long periods and to
recall relevant facts at the appropriate
time;
•Ability to reason inductively;
•Ability to exercise judgment.
•Ability to respond quickly to control
signals and to apply great force smoothly
and precisely;
•Ability to perform repetitive, routine
tasks;
•Ability to store information briefly and
then to erase it completely;
•Ability to reason deductively, including
computational ability;
•Ability to handle highly complex
operations, i.e.to do many different things
at once.
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(Source: Fitts (1951, p. 10, apud Winter 2010))

MABA-MABA Original Illustration
(Source: Fitts (1951, p. 7-8, apud Winter 2010)

Licklider: A Similar View
• Men are noisy, narrow-band devices, but their nervous
systems have very many parallel and simultaneously
active channels.
• Relative to men, computing machines are very fast and
very accurate, but they are constrained to perform only
one or a few elementary operations at a time.
• Men are flexible, capable of "programming themselves
contingently" on the basis of newly received information.
Computing machines are single-minded, constrained by
their "pre-programming.“
• Source: https://www.azquotes.com/quote/707952
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Human –Machine Cooperative Interaction
• Fitts’ Remarks: “The listing of those respects in which human
capabilities surpass those of machines must, of course, be hedged with
the statement that we cannot foresee what machines can be built to do
in the future’ (p 6–7)”
• “We suggest that great caution be exercised in assuming that men can
successfully monitor complex automatic machines and ‘take over’ if
the machine breaks down” (p 11) ( See AI HLEG 2019)
• Licklider (1960) “Man-computer symbiosis is an expected
development in cooperative interaction between men and electronic
computers. It will involve very close coupling between the human and
the electronic members of the partnership. The main aims are:
– to let computers facilitate formulative thinking as they now facilitate the solution
of formulated problems, and
– to enable men and computers to cooperate in making-decisions and controlling
complex situations without inflexible dependence on predetermined programs”6/13/2020 F. G. Filip 24

“The hope is that, in not too many years,
human brains and computing machines will
be coupled together very tightly, and that
the resulting partnership will think as no
human brain has ever thought and process
data in a way not approached by the
information-handling machines we know
today”
Source: https://www.azquotes.com/quote/707952
6/13/2020 F. G. Filip 25

• Paul M. Fitts (1912-1965)• J.C.R. Licklider (1915-1990)
(nicknamed “Johnny Appleseed”)
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Surce
https://aresluna.org/fitts/9.ht
ml
Source:
https://alchetron.co
m/J-C-R-Licklider

A New Perspective
• Dekker et al (2002):
– “The early automation attempts were based on the assumption of
permanent human and machine strengths and weaknesses, and
suggested an often quantitative division of work.
– Quantitative ‘who does what’ allocation does not work because
the real effects of automation are qualitative: it transforms human
practice and forces people to adapt their skills and routines.”
• Rouse&Spohrer (2018).
“We need
– a new perspective to the automation-augmentation continuum and
– create the best cognitive team or cognitive organization to address
the problems at hand”
6/13/2020 F. G. Filip 27

AGENDA
• The Computer as Viewed Yesterday and Today
– A taxonomy of decisions
– Consensus building and Crowdsourceing
• Conclusions
• References
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New Enterprise Paradigms
• Industry 4.0: a set of technologies for the factories of the
future, as a logical evolution from the CIM (Computer
Integrated Manufacturing) movement that started in ‘80s;
• S^3 Enterprise: enhancing sensing, smart, and
sustainability capabilities of manufacturing or service
companies;
• Cloud Manufacturing: that takes full advantage of
networked organization and Cloud Computing;
• Symphonic enterprise, Cyber Physical Manufacturing
Enterprise , Biologicalized Enterprise and so on.
(Sources:Byrne et al 2018; Buchholz 2018; Panetto et al 2019; Vernadat et al 2018;
Zhong et al 2019; )
F. G. Filip 29

Industry 4.0:Needed Capabilities
(de Boer et al 2020)
• “Data, computational power, and connectivity, such
as sensors, the Internet of Things, cloud
technology, and blockchain;
• Analytics and intelligence, ranging from big data
and advanced analytics to artificial intelligence
and knowledge-work automation;
• Human-machine interaction, including virtual and
augmented reality, robotics and automation,
robotic process automation (RPA), and chatbots;
• Advanced production methods such as additive
manufacturing and use of renewable energy”
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“Next Normal”: Building Cappabilities
for Digital Manufacturing
(de Boer et al 2020)
• “A. Data scientist: Creates data structures suitable for analysis and runs
advanced-analytics models to generate insights and predict future events
• B. IT specialists: Manages the technical aspects of automation projects and
technology landscape
• C. Business owner: Provides business input to development, and later owns use
cases
• D. Data engineer/architect: Manages data infrastructure , ensuring robustness of
pipelines and building new features
• E. Solution architects/developer: Designs and develops user experience and user
interfaces
• F. Translator: with deep domain expertise, identifies digital opportunities and
facilitates interface between business and data scientists in iterating models
• G. Digital change lead: Shapes improvement and organizes resources and
requirements to deliver business impact”
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New Decision Problems and
Corresponding Needs
• There are ever more people with various types of
expertise involved in the decision-making activities
carried-out in management and control. This requires
intensive exchange of information and collaboration to
solve the decision problems;
• Decision problems have become more and more
complex, multi-facet ones, and, in a significant number
of cases, should be solved in real time;
• There is a real need and a definite market for advanced
and evolving computer-based tools to support
collaborative decision-making activities.6/13/2020 F. G. Filip 32

Herbert Alexander Simon (1916-2001)
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Source: https://old.post-gazette.com/obituaries/20010210simon2.asp

Decision: A Metaphor
• Herbert Simon:
“A complex decision is like a great river,
drawing from its many tributaries the
innumerable premises of which it is
constituted”.
Source:https://www.azquotes.com/author/13612-Herbert_Sim
6/13/2020 F. G. Filip 34

The Decision:The Adopted Definition
• The decision is the result of human conscious
activities aiming at choosing a course of action for
attaining a certain objective (or set of objectives)
and normally implies allocating the necessary
resources.
• The decision is the result of processing
information and knowledge performed by an
empowered person or group of persons who have to
to make the choice and are accountable for the
quality of the solution adopted to solve a particular
problem or situation.
6/13/2020 F. G. Filip 35

Herbert Simon’s Process Model of
Decision-making
• Phases (Simon 1960):
– Intelligence, which consists of activities such as:
• a) setting the objectives,
• b) data collection and analysis in order to recognize a decision problem,
• c) problem statement;
– Design, which includes activities such as:
• a) identification ( or designing) possible courses of action (alternatives,)
• b) model building, and
• c) evaluation of various potential solutions to the problem;
– Choice, or selection of a feasible alternative, called decision, with
a view to releasing it for implementation.
Remark. Simon (1977) introduced later a fourth step which consists in
implementation of the solution and review of the results.6/13/2020 F. G. Filip 36

6/13/2020 F. G. Filip 37
Source: Filip et al 2017,
p 33 inspired by Simon
1960

Multi-participant Decision-making
Model ( Konaté et al 2019)
• Preparation phase: deﬁning the problem (purpose, the domain, current context,
criteria, and possible constraints) and empowering the decision unit;
• Collective understanding of problem: an extension of the preparation phase
(sharing a common vision of the problem with all the participants and agreeing
on how to implement the designed process );
• Solution generation phase: to produce alternative ideas to solve the problem;
• Negotiation and confrontation of viewpoints: participants elaborate their
contributions present them in order to win the support of the greatest number
• Decision phase: selecting, according to the criteria previously deﬁned, the ideas
which have been approved by most of participants, or which will have made the
consensus within the group;
• Monitoring phase covers all the decision making process so that any problem
can be solved in the allocated time period It includes generating a report on all
decision-making process and ensures their implementation.
6/13/2020 F. G. Filip 38

The Decision: A Taxonomy (Filip et al 2017)
Attribute→
Subclass
↓
Number of
participants
Stable
composition
?
Equal
powers?
Cooperating
?
Human
support
team?
Individual 1 NA NA NA No
Unilateral 1 NA NA NA Yes
Group of
peers
2+ Yes Yes Yes Yes/No
Collectivity 2+ No Yes Yes/No No/Yes
Organizatio-
nal team
2+ Yes No Yes/No Yes/No
6/13/2020 F. G. Filip 39
NA= Non applicable

Consensus-based Multiparticipant Decisions
Two processes (Dong et al 2018):
1. Consensus building
a. Representing participants’ individual preferences representations expressed by
using various models ( cardinal, ordinal, multi-attribute measures, linguistic
preference relations and so on);
b. Aggregating individual preferences ( Weighted Average WA, Ordered WA,
Importance –Induced OWA);
c. Measuring the consensus level as a distance of individual preferences either to
the calculated collective one or as the result of comparing pairs of preferences;
d. Implementing a feedback mechanism to increase the consensus level based either
on a) identifying the participants whose contribution to consensus reaching is
minor or b) aiming at minimizing the number preference revisions.
2. Selecting a recommended solution (or a set of acceptable
solutions).
6/13/2020 F. G. Filip 40

Crowdsourceing: Basic Aspects
• Definition :“Crowdsourcing is a type of participative
online activity in which an individual, an institution, a non-
profit organization or a company proposes to a group of
individuals of varying knowledge, heterogeneity and
number, via flexible open call, the voluntary undertaking a
task. The undertaking of the task, of variable complexity
and modularity, and in which the crowd should participate
bringing their work, money, knowledge and experience,
always entails mutual benefit.” (Estellés-Arolas&Gonzales-
Ladron-de Guevara 2012)
• Platforms: Amazon’s Mechanical Turk3, and CrowdFlower;
Microworker; mCrowd , Climate CoLab, InnoCentive, Spigit, and
Brightidea.and so on ( Wang et al 2016; Metcalf et al 2019))
6/13/2020 F. G. Filip 41

Crowdsourceing-based Decision-Making
• Identification of the problem to be solved or the opportunity to be exploited based
on opinions and predictions collected from the crowd and definition of the task. This
corresponds to Intelligence phase of Simon's process model;
• Task broadcasting to the crowd, performed, in most cases, in the form of an open
call. The crowd may be composed of either enterprise employees (as a modern
technology-supported variant of the old “suggestion box”) or customers and/or other
outsiders;
• Idea generation by the crowd in the form of the set of various action alternatives,
and, possibly, evaluation criteria, It basically corresponds to the Design phase of
Simon’s process model ;
• Evaluation of ideas by the same members of the crowd that generated the idea or
by another crowd or group of experts.;
• Choosing the solution through a voting mechanism or by using a systematic
consensus building and selection procedures.
( Source: Chiu et al 2014)
6/13/2020 F. G. Filip 42

Limitations (Metcalf et al 2019)
• “Serial contributions enabled by platforms might
induce a “herding” behaviour;
• Some decisions require expertise which is not
possessed by the majority of the crowd members;
• Processing an enormous number of contributions
could create problems that can, paradoxically,
make the decision-takers to focus on a limited
number of familiar directions and ignoring the novel
approaches;
• The platforms encourage upvoting the already
crowdsourced ideas and consequently herding
effects.”
A possible solution ==>Human Swarm IntelligenceA possioble 6/13/2020 F. G. Filip 43

AGENDA
• DSS a Class of Evolving Information
Systems
– Classification
– P. Keen’s View of Supporting Systems
• Conclusions
• References
6/13/2020 F. G. Filip 44

DSS: Adopted Definition
( Filip 2008)
• An anthropocentric and evolving
information system which is meant to
implement the functions of a human
support system that would otherwise
be necessary to help the decision-maker
to overcome his/her limits and
constraints he/she may encounter when
trying to solve complex and complicated
decision problems that matter.
6/13/2020 F. G. Filip 45

DSS:Google Scholar (GS) Statistics
• Google Scholar offers a quite broader view because
identifies publications that are not present in other
specialized bibliographic data bases.
• Results of a search of papers with “Decision Support
Systems” in the publication titles
•
6/13/2020 F. G. Filip 46
Year→
Key
Word↓
Total 1980 1985 1990 1995 2000 2010 2015 2019
DSS 12K 36 130 148 243 252 425 477 487
Decision
Support
System
12K 50 148 209 505 283 407 381 350

A Comment about Numbers
W. T. Lord Kelvin (1824–1906):
“I often say that when you can
measure what you are
speaking about and express it
in numbers, you know
something about it; but when
you cannot express it in
numbers, your knowledge is of
a meagre and unsatisfactory
kind” (Lecture: “Electrical Units of
Measurements”, held on 13th May 1883)
Marcus Tullius Cicero (106–
48, B.C.)
“Non enim numero haec
judicantur, sed pondere”
(“The number does not
matter, the quality does”)
6/13/2020 F. G. Filip 47
Photo source:
https://www.dkfindout.com/us/science/famous-
scientists/lord-kelvin/ →

DSS Attributes
• Name: Decision Support System
• Mission to overcome the limits and constraints of the human decision-maker in
making and taking a decision
• Attributes
– Users: decision-makers, their assistants, hired consultants other knowledge
workers
– ,Qualities: anthropocentric, adapted to end-user, and evolving over the time
– Stored knowledge classes: descriptive (about the decision problem/domain),
procedural (solvers), reasoning (about combining system functions),
communication
• Functions: computerized version of functions of the Human Support Systems:
– receiving and accepting decision-maker’s requests
– issuing outputs to decision –maker and requests and orders to third parties
– maintaining and processing its own knowledge
A History of DSS ( Power et al 2019)6/13/2020 F. G. Filip 48

DSS Generations
DSS 1.0 built using timesharing systems;
DSS 2.0 built using mini-computers;
DSS 3.0 built using personal computers and tools like various
spreadsheets, such as Visicalc, Lotus, and Excel;
DSS 4.0 built using DB2 and 4GL (4th generation languages);
DSS 5.0 built using a client/server technology or LAN (Local Area
Network);
DSS 6.0 built using large scale data warehouses with OLAP (OnLine
Analytical Processing) servers;
DSS 7.0 built using Web technologies
DSS 8.0 use computer-based cognitive systems and cloud and mobile
computing.
Adapted from (Power & Phillips-Wren 2011)6/13/2020 F. G. Filip 49

DSS Classifications ( Filip et al 2017)
Criterion Subclass
Envisaged User •Individual;
•Multi-participant (Group, Organizational) DSS
Type of support provided •Passive (data analysis);
•Traditional (“What if…analyses?”);
•Recommender;
•Collaborative;
•Proactive (Smart guidance).
“Dominant” technology
(D. Power’s terminology)
•Data-driven;
•Document-driven;
•[Mathematical] Model-driven;
•Communication-driven (Web-based);
•Knowledge-driven (Intelligent) DSS,
•Service-oriented complex platform
6/13/2020 F. G. Filip 50

Collaboration
• Mandatory Collaboration: Two or more entities collaborate,
because each one working independently cannot deliver the
expected output, such as a product, a service, a decision (Nof et al
2015).
• Optional collaboration Two or more entities might also start
collaborating because they aim at improving the quality of their
deliverables or/and to achieve higher values for all of them (Zhong
et al 2015).
• Attributes of a Collaborative group of humans (Briggs et al 2015):
– a) congruence of methods used by members with agreed common goals,
– b) group effectiveness in attaining the common goals,
– c) group efficiency in saving member resources consumed,
– d) group cohesion, viewed as preserving members’ willingness to collaborate
in the future
6/13/2020 F. G. Filip 51

Human Groups and Crowds
Attributes:
• Place of work: same place or different
places;
• Moment of interaction: synchronous (same
time) or asynchronous (different time);
• Type of interaction: direct or indirect and
mediated;
• Dimension: small teams or large and very
large (crowds) groups.
•
6/13/2020 F. G. Filip 52

Variation Ranges of the Content of
Human Communication
• Cooperation: from friendly and cooperative to
competitive and hostile;
• Intensity: from intense to superficial and uninvolved;
• Dominance: from democratic and equal to autocratic and
unequal;
• Formality: from personal and informal to impersonal and
formal;
• Orientation: from productive and task oriented to
unproductive and no objectives
(Source: Turoff 1991)
6/13/2020 F. G. Filip 53

Major Desired Characteristic Features
of a Group DSS)
• Parallelism meant to avoid the waiting time of participants who
want to speak in an unsupported meeting by enabling all users to
add, in a simultaneous manner, their ideas and points of view;
• Anonymity that makes possible an idea be accepted based on its
value only, no matter what position or reputation has the person
who has proposed it to avoid the leader and followers” effect;
• Memory of the group that is based on long term and accurate
recording of the ideas expressed by individual participants and
conclusions that were reached;
• Improved precision of the contributions which were typed-in
compared with their oral presentation;
• Unambiguous display on computer screen of the ideas.
• Any time and/or any place operation that enable the participation of
all relevant persons, no matter their location
(Nunamaker et al , 2015
6/13/2020 F. G. Filip 54

Winston Leonard Spencer Churchill
(1874-1965)
6/13/2020 F. G. Filip 55
Source: British Government - This is photograph Q 42037 from the collections
of the Imperial War Museums.

Chirchil’s War Room
6/13/2020 F. G. Filip 56
Source:
https://upload.wikimedia.org/wikipedia/commons/b/ba/MapRoomCabinetWarRoomsTrim.jpg

Engelbart’s “Augmented Meeting”(1967)
- A prototype of the Group DSS
6/13/2020 F. G. Filip 57
Source:http://d
ougengelbart.or
g/content/view/
140/000/

A Typical GDSS at NTT in 1996
6/13/2020 F. G. Filip 58
(Source: http://www.wtec.org/loyola/hci/c3_s1.htm )

University of Arizona GDSS
6/13/2020 F. G. Filip 59
Source: Schuff D et al eds (2011) Decision Support: An
Examination of the DSS Discipline. Springer, New York:

• Marcus Tullius Cicero in
De Oficiis ( On [Moral] Duties) , Book V "De Finibus
Bonorum et Malorum" , Chapter 58, 44 BCE:
“Omnium rerum principia parva sunt “
(The beginnings of all things are small/
Everything has a small beginning”)
Source:
https://en.wikiquote.org/wiki/Cicero#De_Officiis_%E2%80%93_On_
Duties_(44_BC),principia%20,%20sunt
6/13/2020 F. G. Filip 60

P. Keen’s View on Evolving Support
Systems
6/13/2020 F. G. Filip 61
User
BuilderSystem
Middle-out
design
Facilitated
implementation
Pressure for
evolution
Evolution of
system function
User learning
Personalized
uses
Cognitive
loop
Implementation
loop
Evolution
loop
“The label ‘Support system’ is meaningful only where the ‘final system’ may
evolve through an adaptive process of design and usage”(Keen1980)

Evolution of DSS DISPATCHER
Family
6/13/2020 F. G. Filip 62
( Source: Filip et al 2017, p 56)

AGENDA
– AI and Cognition as a Service
– Big Data and Analytics
• Conclusions
• References
6/13/2020 F. G. Filip 63

The Time for AI
• Andrew Ng (2017): “Just as electricity
transformed many industries roughly 100 years
ago, AI will also now change nearly every major
industry—healthcare, transportation,
entertainment, manufacturing—enriching the
lives of countless people. I am more excited than
ever about where AI can take us”.
• Mark Cuban: “The world’s first trillionaire will
be an artificial intelligence entrepreneur“
(Clifford 2017).
6/13/2020 F. G. Filip 64

AI emergence- ”a perfect storm”
– a) massive amounts of amassed data collected from
various and multiple sources,
– b) scalable and powerful computer and specialized
software systems, and
– c) the broad accessibility of state-of-the-art
technologies.
(Stoica et al 2017: “A Berkeley View of Systems Challenges for AI”)
6/13/2020 F. G. Filip 65

European Initiatives in AI
• The EU has called for $24 billion to be invested in AI
research by 2020 ( Source: Bughin et al 2018)
• National initiatives in Europe. Examples
– The French government has announced an initiative to
double the number of people involved in AI projects,
invest $1.85 billion for funding research and startups.
– The United Kingdom has published a comprehensive plan
to strengthen the core foundation of AI in an “Artificial
Intelligence sector deal” and stated its aim to lead in the
field of AI ethics ……
EU White Paper on AI 2020): “The key elements of a future
regulatory framework for AI in Europe that will create a
unique ‘ecosystem of trust’ composed of a)people,b)businesses,
and c)society”
–
6/13/2020 F. G. Filip 66

2019:AI HLEG’s Ethics Guidelines for
Trustworthy Artificial Intelligence (I)
• “AI should be
– (1) lawful - respecting all applicable laws and
regulations;
– (2) ethical - respecting ethical principles and
values;
– (3) robust - both from a technical perspective
while taking into account its social
environment. ”
6/13/2020 F. G. Filip 67

2019: AI HLEG’ Ethics Guidelines for
Trustworthy Artificial Intelligence (II)
• Seven Key requirements for Trustworthy AI:
– a) Human agency and oversight ,
– b)Technical robustness and safety,
– c) Privacy and data governance,
– d) Transparency,
– e) Diversity, non-discrimination and fairness,
– f) Societal and environmental well-being,
– g) Accountability
• Six Key dimensions of Deloitte’s Trustworthy AI framework:
a) fair /impartial, b) robust/reliable, c) privacy, d) safe/
security, e )responsible/accountable, f)
transparent/explainable ( Saif, Ammanath 2020)
6/13/2020 F. G. Filip 68

People’s Republic China
• The 13th Five-Year Plan (2016-2020): InternetPlus and
AI plans from 2016 to 2018, and a “new generation AI
plan.”
• China aims to create a domestic AI market of 1 trillion
RMB ($150 billion) by 2020 and become a world-leading
AI center by 2030.
• Three Internet companies (Alibaba, Baidu, and Tencent)
and iFlytek (a company specialized in voice recognition)
have created a “national team” with a view to developing
and deploying AI in several areas such as: autonomous
vehicles, smart cities, and medical imaging.
( Source: Bughin et al 2018)
6/13/2020 F. G. Filip 69

AI to Support Decision-making
• H. Simon (1987):“The MS/OR profession has, in a single generation,
grown from birth to a lively adulthood and is playing an important role in the
management of our private and public institutions. This success should rise our
aspirations. We should aspire to increase the impact of MS/OR by incorporating
the AI kit of tools that can be applied to ill-structured , knowledge rich,
nonquantitative decision domains … “(Two heads are better than
one; the collaboration between AI and OR.
Interfaces , 17(4): 8-15)
6/13/2020 F. G. Filip 70

The Era of Cognitive Computing
• “Now we are at the dawn of a much bigger shift in the evolution of technology—a new era
affecting nearly every aspect of the field. The changes that are coming over the next two
decades will transform the way we live and work just as the computing revolution has
transformed the human landscape over the past half century…. call this the era of cognitive
computing “ (John E. Kelly III & SE Hamm 2013)
• Available Systems: Apple Siri, IBM Watson, Google’s Now, Brain,
AlphaGo, Home, Assistant, Amazon’s Alexa, Microsoft’s Adam,
Braina & Cortana, Baidu’s Minwa, Microsoft’s M, Viv…
6/13/2020 F. G. Filip 71

Cognition as a Service ( CaaS)
• “Engineers predict that by 2055, nearly everyone
has 100 cognitive assistants that “work for
them[….]
• Almost all the people including doctors, physicians,
patients, bankers, policymakers, tourists,
customers, as well as community people greatly
augmented their capabilities by the cognitive
mediators or cognition as a service CaaS[…]
• Cognitive systems can potentially progress from
tools to assistants to collaborators to coaches “.
( Spohrer et al, 2017)
6/13/2020 F. G. Filip 72

James Spohrer
(http://service-science.info/archives/2233 )
6/13/2020 F. G. Filip 73
Service Science: https://service-science.info/archives/author/jim-spohrer
•

Digital Cognitive Systems: From tools
to assistants, collaborators,coaches,
and mediators
Subclass provides
Tool “Data and information ( the system will be able to process
trillions of data and information)
Assistant Knowledge (it will have more knowledge about people)
Collaborator Understanding ( it can understand people’s situation and help
him/her to decide )
Coach Wisdom (it can help our next generation build and re-build from
the scratch)
Trusted cognitive
mediator
Advisory learning and wisdom ( to facilitate value co-creation
and capability co-elevation interactions between entities at
multiple scales)”
6/13/2020 F. G. Filip
Source: Siddike, Spohrer et al (2018)

Steps to Boost Human Performance
6/13/2020 F. G. Filip 75
Source: Siddike, Spohrer et al (2018)

Remarks (I)
• Several capabilities will remain, at least
for the foreseeable future, by the
human agent, because computers
– cannot take responsibility for things they
were not designed for,
– do not have consciousness, and
– are not capable of reflection and cannot
have feelings.
Source: Rouse&Spohrer (2018)
6/13/2020 F. G. Filip 76

Remarks (II)
“When algorithms are at work, there should be
a human safety net to prevent harming people.
Our research demonstrated that in some
situations algorithms can recognize problems in
how they’re operating, and ask for human help.
Specifically, we show, asking for human help
can help alleviate algorithmic bias in some
settings”
( Source: Canetti et al 2019)
6/13/2020 F. G. Filip 77

“Human Agency and Oversight”
(AI HLEG 2019)
“AI systems should empower human
beings, allowing them to make informed
decisions and fostering their fundamental
rights. At the same time, proper
oversight mechanisms need to be ensured,
which can be achieved through
• human-in-the-loop,
• human-on-the-loop, and
• human-in-command approaches”
6/13/2020 F. G. Filip 78

Myths and Misconceptions about AI
• Alexander Linden, the Gartner Research’s VP :
– a) AI works as a human brain,
– b) AI is unbiased,
– c) AI will only replace repetitive jobs, not requiring
advanced degrees,
– d) intelligent machines will learn on their own,
– e) not every business requires an AI strategy, and
– f) AI is the next phase of automation
Source: Bhattacharjee (2019).
6/13/2020 F. G. Filip 79

John McKarthy (1927-2011)-
The man who coined the term Artificial
Intelligence in 1955
6/13/2020 F. G. Filip 80
Source: http://www-
formal.stanford.edu/j
mc/jmccolor.jpg
AI is the science and engineering of
making intelligent machines, especially
intelligent computer programs. It is
related to the similar task of using
computers to understand human
intelligence, but AI does not have to
confine itself to methods that are
biologically observable
(http://jmc.stanford.edu/artificial-intelligence/what-is-
ai/index.htm l

ASI-Artificial Swarm Intelligence
(Rosenberg 2015; Metcalf et al 2019)
A new collaboration technology and software
product ( Swarm® ) inspired from biology and
meant to
• overcome the limitations of AI when facing “known
unknowns”;
• harness the diversity of opinions and facilitate decision
convergence by creating a virtual dynamic “Supermind”;
• maintain the humans in the loop
• use the AI engine to evaluate in real time the confidence
factors and aggregate the opinions parallelly expressed.
6/13/2020 F. G. Filip 81

Alter’s (1977) Taxonomy of DSS (based on the degree
to which the system’s output could directly determine the decision).
Subclass Function
Data oriented
1. File drawer systems To provide access to data items.
2. Data analysis systems To support the manipulation of data by computerized tools
tailored to a specific task and setting /more general tools operators.
3. Analysis information
systems
To provide access to a series of decision-oriented
databases and small models
Model Oriented
4. Accounting and
financial models
To calculate the consequences of possible
Actions ( Simple “What if …” and/or sensitivity analysis)
5. Representational
models
To estimate the consequences of actions on the basis of
simulation models that include causal relationships and accounting
definitions.
6. Optimization models To provide guidelines for action by generating an optimal
solution consistent with a series of constraints.
7. Suggestion models that perform the logical processing leading to a specific
suggested decision for a fairly structured or well-understood task6/13/2020 F. G. Filip 82

Percentage of Publications on DSS
(1971-2001)
Period→
Alter’s taxonomy↓
1971–April 1988 May 1988–1994 1995–2001
Data oriented
File drawer 0% 0% 0%
Data analysis systems 7% 7% 0%
Analysis information
systems 11% 17% 4%
Model oriented
Accounting model 7% 3% 1%
Representation model 40% 25% 21%
Optimization model 28% 38% 51%
Suggestion model 6% 10% 23%
100% 100% 100%
6/13/2020 F. G. Filip 83
Source: Eom&Kim (2002)

Today: Papers on Business Analytics in
Academic Journals (2001–2018)
6/13/2020 F. G. Filip 84
Source: Power et al (2018)

Shi (2018):
• “Big Data pushes the change of
Management Science: from traditional
‘model-driven’ and ‘case-driven’ to ‘data
driven’ decision making”
6/13/2020 F. G. Filip 85

Today in Industry
• World Economic Forum (WEF 2019) and McKinsey
identify 16 “top-performing factories […] “lighthouses”-
the world’s most advanced sites implementing
technologies of the Fourth Industrial Revolution.
• Big-data decision making is a value driver for impact
at scale, one of differentiators that transform how
technology is implemented, how people interact with
technology, and how it affects business decisions […]
• Decisions are not hypothesis-driven, but rather, based on
big data deciphered by pattern recognition—and not by
humans
6/13/2020 F. G. Filip 86

“Data Revolution”
• Kai-Fu Lee of Sinovation Ventures:
“ Four inputs that power the data revolution:
– abundant data,
– hungry entrepreneurs,
– AI scientists and and
– AI-friendly policy environment”.
Source: Phillips (2019)
• + The hardware and tools required to process
data
6/13/2020 F. G. Filip 87

Big Data Attributes
• Volume measures the amount of data available and accessible .
• Velocity is a measure of the speed of data creation, streaming and
aggregation.
• Variety measures the structural heterogeneity of data representation:
numeric, textual, audio, video, structured, unstructured and so on
(introduced by SAP).
• Value is a measure of usefulness and usability in decision making
(introduced by ORACLE)
• Complexity measures the degree of interconnectedness,
interdependence in data structures and sensitivity of the whole to local
changes (introduced by SAP) .
• Veracity measures the confidence in the accuracy of the data
(introduced by IBM).
Source: Kaisler et al (2013); Gandomi& Haider (2015 )
6/13/2020 F. G. Filip 88

Alternative Definitions
• Large, diverse, complex, vertical, and/or distributed data
sets based on instruments, sensors, Internet transactions,
email, video, and click streams, and/or all other digital
sources available (Source: NSF 2012, apud Shi 2018).
• “Big data is collections of data with complexity, diversity,
heterogeneity and high potential value, which are difficult
to process and analyze in reasonable time. Big Data is new
type of Strategic Resource in digital era and the key
factor to drive Innovation, which is changing the way of
Human being’s current production and living.” (462th
Xiangshan Science Conference, May 2013 apud Shi 2018))
6/13/2020 F. G. Filip 89

Five phases for extracting insights
from Big Data
6/13/2020 F. G. Filip 90
Source: Gandomi&Haider (2015)

Data Science
• Shi (2018) of CAS (Chinese Academy of Sciences)
– “Big data is based on Data Science;
– Data science is the science of data collection, management,
transformation, analysis, and application, and its core is to
study the acquisition of knowledge from data.
– Data science has begun to gradually replace the known business
intelligence and business analysis in applications.Data analysts
are called ‘data scientists”
• Carle Idoine et al (2019) of Gartner:
– Data Science is “a cohesive software application that offers a
mixture of basic building blocks essential for creating all kinds of
data science solutions, and for incorporating those solutions into
business processes, surrounding infrastructure and products”
(Cohesive= the basic building blocks are well integrated into a single platform)
6/13/2020 F. G. Filip 91

Data Science in The Context of Covid19
• Typical applications ( Latif et al 2020)
– Risk Assessment and patient prioritization;
– Screening and diagnosis;
– Simulation and modelling;
– Contact tracing;
– Understanding social interventions;
– Logistical planning and economic
interventions;
– Automated primary care.
6/13/2020 F. G. Filip 92

Providers of Platforms (Gartner 2019)
• Leaders: KNIME (SW), Rapid Miner, Sa, TIBCO (“Leaders have a
strong presence and significant mind share in the data science and
ML(Machine Learning) market) ;
• Challengers : ALTERYX, Dataiku (“Challengers have an
established presence, credibility, viability and robust product
capabilities. They may not, however, demonstrate thought
leadership and innovation to the same degree as Leaders”);
• Visionaries : Databriks, DataRobot, Google, H2O.ai, IBM Math
Works, Microsoft (“Visionaries are typically relatively small
vendors or newer entrants representative of trends that are shaping,
or have the potential to shape, the market”).
• Niche Players: Anaconda, Datawatch, Domiino, SAP ( D)(“Niche
Players demonstrate strength in a particular industry or approach,
or pair well with a specific technology stack.”)F. G. Filip 93

Skills for Creating a Data Culture
6/13/2020 F. G. Filip 94
1 Business
leaders
2 Delivery
managers
7 Analytics
translators
4 Visualisation
analysts
5 Data
engineers
3 Workflow
integrators
8 Data
Scientists
6 Data
architects
Business
Skillsls
Analtics
Skills
Technology
Skills
Source: Diaz et al 2019
y

An Application: iDSS an integrated platform
for Group Decision Support:the Server
(Candea and Filip 2016)
6/13/2020 F. G. Filip 95

iDSS Levels
6/13/2020 F. G. Filip 96
a) B( business) aaS level; b) S( Software) aaS level; c) P(latform)
aaS level; d) I( infrastructure) aaS level (Radu et al. 2014)

A Final Remark
“ The ‘human system’ and the ‘tool system’ are equally important in CSCW. […]
To take advantage of the radical emerging tool system inventions associated with CSCW, it is
inevitable that the evolution of the human system will begin to accelerate. In the optimum design,
either a tool system or a human system is dependent on the match it must make with the other. The
high degree of dependence implies that a balanced co-evolution of both is necessary. The bind we
are in is that our society encourages and rewards progress in technological and material sense and
often ignores the human and social implications of that progress.”
(Engelbart, Lehtman 1988)
6/13/2020 F. G. Filip 97

Other Open Problems
• Digital Humanism vs Dataism
– Digital humanism seeks to enable people to achieve
things they never believed possible or redefine the way
their goals can be achieved (Pettey 2015 )
– Dataism has the potential to replace humanism and its
worship of the individual with the ‘information flow’ as
the ultimate goal to which all other activities, efforts
and desires will become subservient (Harari 2016)
• Ethical concerns in big data management (Naif
2020)
• Concepts drift in streaming big data (Lu et al
2019)
• ……………………………….6/13/2020 F. G. Filip 98

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and solutions. https://doi.org/10.1002/cpe.3789
• WEF ( 2019) Fourth Industrial Revolution beacons of technology and
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• Weldon D (2019) 17 top data science and machine learning platforms .
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• Zhong R et al (2019) Intelligent Manufacturing in the Context of Industry
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6/13/2020 F. G. Filip 108

Thank-you for your attention!
6/13/2020 F. G. Filip 109
https://acad.ro/cv/FilipF/FGF-CV-en.pdf

The Development of Decision Support Systems

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