Requirements engineering for sociotechnical
systems: Case study of an Airline Operations
Control Center
Nico Zimmer Kuldar Taveter
The Boeing Company Inc. Institute of Computer Science
Neu-Isenburg, Germany University of Tartu
nico.zimmer@boeing.com Tartu, Estonia
kuldar.taveter@ut.ee
Abstract—This paper is concerned with requirements from the AOCC stakeholders by following the elicitation
engineering for sociotechnical systems. The paper describes and approach described in [6] and [11].
analyzes the requirements for the sociotechnical system of the
Airline Operations Control Center (AOCC). This is done for The rest of this paper is structured as follows. In Section
studying the social part of the AOCC by means of agent-based II, the holistic requirements engineering framework for STS
simulation of AOCC employees with different personality used in the work is described. In Section III, the framework is
profiles. The requirements are mapped to the viewpoint applied to modelling the behavior, organization, and
framework for holistic requirements elicitation and information perspectives of the AOCC. In Section IV, the
representation at different abstraction layers and from design of the simulation system for the human subsystem of
complementary perspectives. The design of an agent-based the AOCC is briefly described, based on the requirements.
simulation system based on the requirements is briefly The conclusions are drawn and directions for future work are
described. Finally, the benefits of this kind of requirements set in Section V.
engineering approach are brought out and the directions for
future work are set. II. REQUIREMENTS ENGINEERING FOR STS
Keywords—sociotechnical system, requirements engineering,
STS should be designed in a holistic fashion [11]. Holistic
model, abstraction layer, perspective, viewpoint, agent-based design means, among other things, that the software system to
simulation be designed should be viewed through complementary lenses
of its social, informational, and behavioral context. For this
I. INTRODUCTION purpose, we propose to apply a methodology that is centered
on the viewpoint framework defined in [13]. The viewpoint
This paper is concerned with studying complex
framework consists of a matrix with three rows representing
sociotechnical systems. A sociotechnical system consists of
the abstraction layers of problem domain analysis, design, and
tasks, people, organization, and technology [1]. In other
implementation and three columns representing the
words, sociotechnical systems comprise humans, social,
perspectives of organization, information, and behavior. Each
organizational, and technical factors [2]. Sociotechnical
cell in this matrix represents a specific viewpoint, such as
system (STS) is defined as a software intensive system that
organization analysis, information design and behavior
has defined operational processes followed by human
implementation. The perspectives of organization,
operators and which operates within an organization and
information and behavior are respectively geared towards
comprises both social and technical aspects [3]. STS consists
eliciting and representing social, informational, and
of humans, software, and hardware [3].
behavioral contexts. In this paper, we address the viewpoint
The aviation domain contains many good examples of aspects of the system analysis layer because the focus of this
sociotechnical systems. First, modern aircraft should be paper is on requirements engineering for STS. Each of these
viewed as STS [4]. In our previous work [5-6], we have perspectives – information, interaction, and behavior – can be
studied airports as STS. This paper studies an Airline represented by appropriate models. The models at the
Operations Control Center (AOCC) as STS. abstraction layer of system analysis represent the
requirements elicited for the STS. Likewise, the models at the
The research literature indicates that the agent-oriented abstraction layers of design and implementation respectively
paradigm is a natural metaphor for studying complex STS represent the design and implementation of the STS. Since this
because it enables to elicit and represent requirements for both paper is concerned with requirements engineering, we focus
the social and technical aspects [6-9]. The purpose of this on the models required for capturing the highest abstraction
paper is to describe and analyze the requirements elicited for layer of problem domain analysis and only marginally treat
designing and implementing an agent-based simulation the models needed for addressing the abstraction layers of
system [10] for the important social part of the AOCC STS – design and implementation.
AOCC employees responsible for dispatching flights. As we
have shown in [6], for successful agent-based simulation of The viewpoint framework has been previously applied for
some aspect of STS, the requirements for the greater STS requirements engineering as described in, e.g., [6, 9, 14-17].
should be elicited and modelled. Considering this, this paper The methodology for filling in the viewpoint framework,
describes the requirements elicited for the whole STS of including the order of filling, is described in [6]. The
AOCC. The requirements described in the paper were elicited advantages of using the methodology based on the viewpoint
framework are that it enables to model requirements for STS
Copyright © 2022 by the author. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
at three abstraction layers and from three complementary III. REQUIREMENTS FOR THE AOCC
vertical perspectives. Another advantage is that the models
included by the viewpoint framework are rooted in simple A. Goal Models and Motivational Scenarios
principles, which makes the models palatable for non- Goal models are relevant for capturing the purpose and
technical stakeholders. The viewpoint framework is goals of the STS. Goal models belong to the viewpoint aspect
represented in Table I. This paper is focused on the models of of behavior analysis. According to Sterling and Taveter [12],
organization, information, and behavior analysis. goal models include functional goals denoted by rhomboids
Requirements models of AOCC of the mentioned viewpoint that represent functional requirements of the system, quality
aspects are presented and explained in Section III. Design goals (clouds) that model non-functional requirements of the
considerations of the agent-based simulation system at the system, and roles (stick figures) that describe capacities or
system design layer are presented in Section IV. Finally, the positions of the system required to achieve the goals. There
simulation system is implemented at the layer of system can also be relationships between functional goals (solid
implementation, which falls outside the scope of this paper. lines), and between goals and quality goals (dashed lines). For
easier reading, we indicate in this paper goals and quality
Table I. The viewpoint framework
goals as highlighted in the italic font, and roles in the bold
Viewpoint aspect font.
Abstraction Organization Information Behavior The overall goal or purpose of airline operations control
layer
System Role models, Domain model Goal model, is to Maintain & control the day of operation network
analysis organization Motivational schedule to provide an Efficient, safe, quality & profitable
model scenario network operation to the airline’s customers – mainly its
System design Models of platform-independent design passengers, but potentially also to its cargo customers. The
System Platform-specific implementation goal model of airline operations control is shown in Figure 1.
implementation
Operations Control
Efficient, safe, quality & Maintain & control
profitable network operation day of operation
network schedule
On-time and with
minimal Execute
disruptions network schedule
Ground Maintenance Flight Passenger Crew
Control Control Dispatch Control Control
Manage Manage Manage Manage Manage Manage
passenger &
ground operations maintenance operations flight operations disruptions revenue crew
Turnaround efficient
and on-time
Aircraft legal and
servicable
Flight cost-effective,
legal and on-time Quality service, cost-effective &
minimal impact on network schedule
Customer satisfied &
revenue maximized Crew legal, sound and
efficient
Figure 1. The goal model of airline operations control
Table II. The motivational scenario of airline operations control done in through collaboration between the AOCC sub-
roles. The following activities are included:
Scenario Airline Operations Control a) managing ground operations by preparing the
name aircraft on the ground and conducting weight and
balance calculations
Scenario The purpose of the airline operations control is to maintain
description and control the day of operation network schedule. This is
b) managing maintenance operations by reviewing their own quality goals to ensure the entire network operation
aircraft status, and tracking and solving flow. All of these quality goals contribute to the achievement
maintenance issues
of the quality goal On time and with minimal disruptions. A
c) managing flight operation by preparing,
dispatching, and following flights more narrative-like way of representing the meaning of a goal
d) managing passenger and revenue by tracking, re-
model is motivational scenario [13]. Motivational scenarios
booking, and accommodating passengers belong to the viewpoint aspect of behavior analysis. Table II
e) managing crew by monitoring, tracking, and presents the motivational scenario of airline operations control
recovering crew members and how agents fulfill their corresponding roles to achieve
f) managing disruptions by gaining situational their goals. Additional goal models and motivational scenarios
awareness and optimizing and executing proper for a day of operation are available in [18].
solutions
Since the operational flow is regularly disturbed by
Quality Executing and controlling the day of operation network
description schedule should ensure an efficient, safe, and high-quality
disruptions which lead to irregular operation, a very important
service to airline customers – the passengers. The airline’s sub-goal shown in Figure 1 is Manage disruptions.
day of operation should be at the same time profitable, Disruptions should be managed while they occur, but still
solvable, and punctual without large disruptions or delays ensuring a Quality service, cost effective, and minimal impact
to maintain the airline’s reputation.
on flight schedule. The responsibility and final decisions of
Figure 1 reflects that achieving the overall goal of airline disruption management lie with the duty manager performing
operations control is the responsibility of the duty manager the role of Operations Control, but collaborative decision
performing the role Operations Control. To offer a service to making is performed amongst all the sub-roles of the AOCC
airline customers, Operations Control is also responsible for represented in Figure 2 to find an optimal solution. Figure 2
also shows the sub-goals of Manage disruptions. Different
achieving the goal Execute network schedule with the quality
aspects of managing disruptions are represented by the sub-
goal to deliver the service On time and with minimal
goals Gain situational awareness, Optimize solution, and
disruptions. The responsibilities for achieving the sub-goals
Execute solution with their respective lower-level sub-goals.
Manage ground operations, Manage maintenance operations,
Table III shows the motivational scenario of disruption
Manage flight operations, Manage passenger & revenue, and
management.
Manage crew are delegated to the respective roles Ground
Control, Maintenance Control, Flight Dispatch, Passenger
Control and Crew Control. Performers of these roles follow
Flight Operations
Dispatch Quality service, cost- Control
Crew effective and minimal
Passenger
Control impact on flight Control
schedule
Maintenance Ground
Control Control
Manage
disruptions
Gain Easy to
Easy and fast to Optimize Easy to
situational compare Execute solution
see impact solution disseminate
awareness
Develop Evaluate Communicate Apply
Identify issue Gather impact
solution solution decision decision
Minimize (indirect) cost Minimize impact on
associated with disruption passenger convenience
Figure 2. The goal model for managing disruptions
Table III. The motivational scenario of disruption management Scenario Disruptions need to be handled proactively to avoid delays
description and additional costs. Managing disruptions potentially
Scenario Disruption Management involves all sub-roles of the AOCC. The responsibility and
name final decisions lie with the duty manager performing the
role of Operations Control. Disruption management personality features Conscientiousness (C) and Agreeableness
includes the following activities: (A) have been identified as valid occupational performance
a) gaining situation awareness by identifying and predictors [18]. C and A may have positive or negative
understanding the issue and its impact on the
disruption, e.g., does it affect my fleet, crew, influence (indicated by the ‘+’ or ‘-’ sign) on the collaborative
and/or passengers? team-based decision making. At the same time, Openness,
b) optimizing a solution by evaluating different Extraversion and Neuroticism are expected to be neutral
solutions and developing one proper solution (indicated by the ‘n’ sign), based on Peters [20] who compared
together with the involved AOCC roles the personality profiles of an AOCC employee population
c) executing the solution by communicating a with a norm population.
decision to relevant stakeholders and applying
the decision Quality service,
cost-effective and
minimal impact on
Quality Disruption management should allow to gain situation Technical network schedule Social
dimension
description awareness, identify, and understand the impact, make fast dimension
decision by the controllers, but still ensure a quality service
Timely available Collaborative
for passengers. The solution should be cost-effective and information team-based
decision making
have a minimal impact on the overall network schedule,
minimizing delays.
Individual
B. Analyzing Quality Goals Organizational
DNA personality
Team
taxonomy
The analysis of quality goals constitutes an important tool <
for STS requirements analysis. According to Sterling and n +/- n +/- n
Taveter ([13], p. 140), quality goals can contribute positively Openness
Conscien-
Extraversion Agreeableness Neuroticism
tiousness
or negatively to the achievement of a functional goal. In
Figure 1, the quality goal On-time and with minimal
Figure 3. Social and technical dimensions of achieving the Manage
disruptions is associated with the functional goal Execute disruptions functional goal
network schedule, representing the positive plan at the
beginning of a day of operation. Figure 1 reflects that in case C. Role Models
of no disruption or minimal disruption, the following quality
goals contribute positively to the quality goal On-time and Role models describe each role of STS in terms of its
with minimal disruptions: Turnaround efficient and on-time; responsibilities and constraints required to achieve the goals
Aircraft legal and serviceable; Flight cost-effective, legal and of the STS. Role models belong to the viewpoint aspect of
on-time; Quality service, cost effective & minimal impact on organization analysis. Table IV contains the key
network schedule; Customer satisfied & revenue maximized; responsibilities and constraints for the roles needed for
Crew legal, sound and efficient. However, the situation achieving the goals of disruption management modelled in
changes in case of a significant disruption. In such a case, each Figure 2.
of these quality goals can also exert a negative influence on Table IV. Role model for AOCC in case of disruption management
the achievement of the overall quality goal On-time and with
minimal disruption. The reason is that cost, time, and resource Role name Responsibilities Constraints
availability continuously stand in conflict which each other Operations Aircraft recovery: Dependency on
during a day of operation and such conflicts become critical Control Find options for maintenance based on
during disruption management. Handling conflicts in goal- alternatives availability of a substitute
aircraft or maintenance
oriented requirements is a separate issue that we have resources and complexity of
addressed more in detail in [19]. the required maintenance
In the socio-technical context, it is interesting to further Crew Control Crew recovery: Find Crew duty times,
alternative resources availability of stand-by
analyze how the technical and social dimensions may and keep crew duty resources, and dependencies
influence the achievement of the quality goals. Figure 3 times legal on other flights
elaborates the social and technical dimensions of the quality Maintenance Aircraft: Ensure Availability of maintenance
goal Quality service, cost effective, and minimal impact on Control legality resources
flight schedule that is associated with the functional goal Ground Control Turnaround process: Usually, the third party to
Manage disruptions. Each of the other quality goals could be Accelerate which the turnaround
procedure has been
elaborated the same way. The technical dimension is rather outsourced has no incentive
easy to grasp. If all technical systems deliver the information to speed up turnaround
in a timely manner and without failure, the overall impact of Passenger Passenger recovery: Availability of alternatives –
the technical dimension would be positive. The social Control Rebook and flights or accommodation.
dimension is much more complex. This is because the team accommodate Reputation is usually
passengers negatively influenced
effectiveness framework, introduced in Section 2.3.1 of [18],
includes three factors which may enable or impede the overall Flight Dispatch Flight plan: Speed A flight plan change should
up en route be approved by the Air
effectiveness: team member characteristics (personality), Traffic Control
team-level factors (team taxonomy, e.g., cohesion), and
organizational or contextual factors (organizational identity, D. Organization Model
e.g., organizational structure of an airline). All these factors Organization model represents relationships between the
influence collaborative team-based decision making. As has roles. Organization models belong to the viewpoint aspect of
been concluded in Section 2.3.2 of [18], personality has a organization analysis. A relationship can be defined as a
considerable impact on team effectiveness. The Five-Factor control, benevolence, or peer relationship according to
Model (FFM) of personality profiles and particularly the Sterling and Taveter (([13], p. 75). Figure 4 represents the
organization model of the AOCC. The upper part of Figure 4 Maintenance Control, and Passenger Control. All of them
shows the relevant contextual organizational roles Ground are peers to each other and are controlled by the Operations
Operations, Crew Operations, Flight Operations, Control to maintain and control the day of operation. Flight
Maintenance & Engineering, and Revenue & Passenger Dispatch is a special role because it has the peer relationship
Management. These contextual roles represent with all the other organizational roles. Additional sub-roles
organizational groups. They aggregate the key actors from the denoted by white-filled figures in the lower part of Figure 4
AOCC and outside actors related to the AOCC. The sub-roles are involved in a day of operation. They are essential for
of the AOCC denoted in Figure 4 by dark-filled roles include achieving the goals of the AOCC and disruption management
Ground Control, Crew Control, Flight Dispatch, modelled in Figures 2 and 3.
Ground Crew Flight Maintenance & Revenue & Pax
Operations Operations Operations Engineering Management
aggregation
aggregation aggregation aggregation
aggregation Operations
Control
aggregation aggregation
Crew Maintenance
Control isControlledBy Control
isControlledBy isControlledBy
isControlledBy
isControlledBy
isControlledBy
isPeer Passenger
Control
isPeer isPeer
isPeer
Ground Flight isControlledBy
Control Dispatch
isPeer isPeer
isControlledBy isControlledBy isControlledBy isControlledBy
Crew AirTraffic Line
Member Controller Forman
Ramp Weight & Gate Check-In
Agent Balance Agent Agent
isBenevolentTo
isControlledBy
Legend
inheritance inheritance
isControlledBy isControlledBy
AOCC Role
isPeer isControlledBy
aggregation
Fueling Cleaning Catering Loading Cabin Cockpit Tower Line inheritance
Member Member Controller Mechanic
relationship
Figure 4. Organization model of AOCC
E. Domain Model controlled by the performers of different roles. For example,
Domain model represents the domain knowledge to be the Maintenance Control System (MCS) is an information
stored and handled within STS. Domain models belong to the system overseen by Maintenance Control to track aircraft
viewpoint aspect of information analysis. Domain model health status and provide Operations Control with the
consists of domain entities and relationships between them information about the aircraft airworthiness and legality. The
([13], p. 76). The subject of the domain model represented in MCS also stores aircraft maintenance data, such as Minimum
Figure 5 is the knowledge shared and handled within the Equipment List (MEL) and Configuration Deviation List
AOCC. The AOCC domain model also includes the (CDL), which are provided by a Crew Member before or
knowledge about disruptive events, their impact on cost and after the flight, or through a Line Mechanic during the line
time, and the potential solution developed to mitigate the maintenance checks. Finally, the MCS stores and provides
disruption. Additionally, the AOCC domain model represents information about the overall aircraft performance which is
technical systems – information systems – used by the AOCC being utilized by the Flight Dispatch during the flight
as resources, which are denoted in the figure as AOCC planning process and is forwarded to another information
environments by underlying their names. Resources are system – the Flight Planning System.
Domain Entity developes
Disruption Cost/
Environment generates
Operations Control recognizes Delay
Role monitors
contain
relationship
Disruption Mitigates/solves
Lies in Network Schedule
cues Management System
lies in
relationship
Cockpit Member
A/C Movement Mitigates/
reviews Control System Disruption Event Disruption Solution
solves
accepts aggregation
Airport Data
developes
Schedule Updates status affects
Operational describes is assigned to Crew Control Signs-in
Wx/NOTAM analyses calculates Flight Crew Roster Lies in
Flight Plan System
Data
approves
files
Flight Dispatch tracks
Crew Member
ATC Flight Plan cleares
developed developes
Situated in
informs developes
provides accepts
Flight Planning
Fuel amount provides
System
is assigned to Crew Control Ground Control
ATC overseas
provides inputs developes
Used in
has needs involves
Used in Aicraft Performance Aircraft Servicing
Check-in Agent Cargo Planner
provides
Passenger data Cargo load data Payload has reviews
Fueling
updates Maintenance Control Cleaning
updates & System Catering
commits updates tracks
provides
Maintenance Data Line Maintenance Loading
Situated in updates
(MEL /CDL)
Gate Agent Maintenance Control
Departure Computer
System updates
tracks
developes
provides
Passenger Control
Load & Trim
calculates executes coordinates
Sheet
Weight & Balance Ramp Agent
Figure 5. Domain model of AOCC
IV. FROM REQUIREMENTS TO DESIGN modelled by the corresponding subgoals Choose passenger
As was stated in Section I, the requirements described in option, Choose crew option, and Choose aircraft option. The
this paper serve the purpose of designing and implementing roles responsible for achieving these goals are respectively
an agent-based simulation system [10] for the important social Passenger Control, Crew Control, and Operations
part of the AOCC STS – AOCC employees responsible for Control. Each of the subgoals is characterized by the
dispatching flights. As we pointed out in Section III.B, the corresponding quality goals that are modelled from the
technical dimension of STS is rather straightforward, as it is perspectives of the relevant roles. For example, the quality
embodied in the corresponding information systems, such as goals Minimal passenger compensation, Protected passenger
various systems represented in Figure 5. Those systems make and Minimal passenger delay capture the decision-making
recommendations for solving disruptions in one way or criteria for choosing the passenger option from the perspective
another, effectively acting as decision-support systems. The of Passenger Control. Figure 6 represents more elaborate
social dimension is much more complex because of the options for each of the main solution types as lower-level sub-
inherent complexity of human decision-making processes. goals under the second-level goals Choose passenger option,
Considering this, the design stage of our work is concerned Choose aircraft option, and Choose crew option.
with designing simulation experiments to be performed with Minimal
Develop
solution
the social part of the AOCC STS. For this purpose, passenger
compensation Crew
Control Protected
requirements for the greater STS described in Section III had
schedule &
Minimal crew Protected
maximizedrevenue
Protected delay & costs crew
Passenger
to be represented and analyzed. Simulation experiments were
passenger Control Operations
Control
Minimal flight
designed as agent-based simulations of the human subsystem Minimal
passenger
Choose
crew option
delay & costs
of the AOCC, where software agents performed the AOCC delay
Choose
roles that are usually performed by humans [18]. Since this
Use crew Choose
passenger option Accept delay
on vacation aircraft option
paper is concerned with requirements engineering for STS, we Change pax on
Cancel flight
Use day off Swap
different flight Delay flight
next just briefly outline the steps of the simulation system same airline
drew aircraft
design, leaving treating the simulations for a different paper. Change pax on
different flight
Proceed without
crew
Exchange with
crew another Reroute flight
Book
wet -lease
other airline flight
To manage a disruption, there are three basic kinds of Keep pax on Propose aircraft
Use nearest
reserve crew Cancel
Join flights
solutions: passenger option, crew option and aircraft option. delayed flight change
at hub flight
Those options are captured by the goal model shown in Figure Cancel flight
Use crew with
free time
Use reserve
at airport
6, which elaborates the Develop solution functional goal
represented in Figure 2. In Figure 6, the three solutions are Figure 6. Goal model for developing a disruption solving solution
The goal model shown in Figure 6 is crucial as the foundation
to develop the goal-driven behavior models of humans – the
AOCC personnel – for simulating the decision-making
process of disruption management. In Figure 6, some
functional goals, such as Cancel flight, appear under several
second-level functional goals. Since a given functional goal
has the same meaning, no matter where it is in a goal tree, the
decision to pursue one or another lower-level goal denoting a
particular disruption solution is made based on evaluating
how well the associated quality goals are met. By combining
functional goals and quality goals pertaining to some role,
such as Passenger Control, behavior models for agents
playing the corresponding roles were developed which
Figure 7. Disruption solution matrix with the Cancel (C), Reroute (R), and
address the behavior perspective of the system design layer of
Delay (D) options
the viewpoint framework shown as Table I. For executing the
behavior model, a scenario of disruption management was CONCLUSIONS
defined that offered three different simplified solutions –
cancel, reroute, and delay – to be chosen through collaboration This work discussed requirements engineering for
between the roles Passenger Control, Crew Control, and sociotechnical systems, using the example of the AOCC. We
Operations Control. specifically applied the viewpoint framework for ensuring
For evaluating the achievement of the quality goals holistic requirements representation from complementary
defined for the corresponding roles, the agent cost function perspectives and represented the requirements by the models
was derived based on [21]. The function evaluates each included by the system analysis layer of the viewpoint
disruption management solution based on its cost and framework. The requirements analysis addressed the
probability of success. For choosing one or another solution organization, information, and behavior analysis viewpoint
based on the return value of the agent cost function, the agent- aspects of the AOCC. The application of the goal-oriented
based simulation also considers the simulated FFM modeling approach allowed to differentiate between
personality profile of the AOCC decision-maker simulated by functional goals and quality goals. This is beneficial as
the agent. For example, whether the simulated decision-maker qualitative concerns could be matched to personality-driven
accepts the solution such as the passenger solution described behavioral patterns, which, in turn, could be mimicked by
in Table V, also depends on the FFM personality profile of the agent-based simulations. This is the main novelty of our
decision-maker, considering the goal-oriented planning approach, as this kind of agent-based simulation of a social
process of a human [22-24]. part of STS has been done for the first time according to the
best of our knowledge. Importantly, a prerequisite for
Table V. Interpretation of goals for the Passenger Control role
adequate simulations of a social part is eliciting and
Role Func- Quality Plan Solution representing the requirements for the greater STS surrounding
tional goals asses- the social part to be simulated. Engineering requirements for
goal sment the greater STS is the main topic of this paper. Moreover, our
Cancel Delay Reroute
approach allows to represent technical dimension of STS
Pas- Choose Minimal Proba- Low High High
senger pas-
passenger
bility
equally well. For that matter, in the future work we plan to
compensation,
Control senger Protected Effort Low High Med model the technical part of the AOCC STS in more detail and
option passenger, integrate into holistic modelling and simulation of decision-
Minimal
passenger delay making processes within the AOCC. The result would be a
simulation of the AOCC as a whole which can be used for
In the simulations, qualitative values ‘low’, ‘high’ and
exploring and optimizing the existing airline operations
‘med’ that were used in choosing a disruption solution, such
control solutions by means of agent-based and other types of
as the passenger solution described by Table V, were
computer-based simulations.
interpreted as quantitative values [18].
Like the agent cost function for a human decision-maker, ACKNOWLEDGMENT
the system cost function represents the disruption solution The contribution by the second author to this publication
offered by the information systems of the AOCC. This way, has been funded by the IT Academy program of the European
we put equal weights on the disruption solutions of the STS Social Fund.
generated by (simulated) humans on one hand and (simulated)
information systems on the other. The overall decision- REFERENCES
making process is a collaborative majority-based decision- [1] M. T. O'Hara, R. T. Watson, C. B. Kavan, „Managing the three levels
making process by the involved agents. The majority-based of change,“ Information Systems Management, vol. 16, pp. 63-70,
decision is made by applying the agent selection function. 1999.
Figure 7 illustrates the decision-making process with the [2] G. Baxter, and I. Sommerville, „Socio-technical systems: From design
methods to systems engineering,“ Interacting with Computers, vol. 23,
simplified cancel, delay and reroute options to be chosen no. 1, 2011 pp. 4-17, 2011.
between that are offered by simulated humans and simulated [3] I. Sommerville, Software Engineering (Ninth Edition), London,
information systems. England: Pearson, 2009.
[4] P. M. Salmon, G. H. Walker, and N. A. Stanton, „Pilot error versus [15] I. Shvartsman, K. Taveter, M. Parmak, and M. Meriste, “Agent-
sociotechnical systems failure: a distributed situation awareness oriented modelling for simulation of complex environments,” in
analysis of Air France 447,“ Theoretical Issues in Ergonomics Science, Proceedings of the International Multiconference on Computer Science
vol. 17, no. 1, pp 64-79, 2016. and Information Technology (IMCSIT 2010), pp. 209-216, IEEE,
[5] L. Sterling, and K. Taveter, „Event-based Optimization of Air-to-Air 2010.
Business Processes,“ in N. Stojanovic, A. Abecker, O. Etzion, and A. [16] I. Shvartsman, and K. Taveter, “Agent-oriented knowledge elicitation
Paschke, Eds., 2009 AAAI Spring Symposium: Intelligent Event for modeling the winning of “hearts and minds”,” in 2011 Federated
Processing, The AAAI Press, pp. 80-85, 2009. Conference on Computer Science and Information Systems (FedCSIS),
[6] T. Miller, B. Lu, L. Sterling, G. Beydoun, and K. Taveter, pp. 605-608. IEEE, 2011.
“Requirements elicitation and specification using the agent paradigm: [17] K. Taveter, and A. Norta, “Agile software engineering methodology
the case study of an aircraft turnaround simulator,” IEEE Transactions for information systems’ integration projects,” in Future Data and
on Software Engineering, vol. 40, no. 10, pp. 1007-1024, 2014. Security Engineering (FDSE 2017), pp. 215-230. Springer, Cham,
[7] P. Bresciani, A. Perini, P. Giorgini, F. Giunchiglia, and J. Mylopoulos, 2017.
“Tropos: An agent-oriented software development methodology,” [18] N. Zimmer, “Socio-technical modeling and simulation of airline
Auton. Agents Multi-Agent Syst., vol. 8, no. 3, pp. 203–236, 2004. operations control,” Doctoral thesis, Technische Universität
[8] E. Yu, P. Giorgini, N. Maiden, and J. Mylopoulos, Social Modelling Braunschweig, Germany, 2020.
for Requirements Engineering. Cambridge, MA: MIT Press, 2011. [19] I. Gambo, and K. Taveter, “A Pragmatic View on Resolving Conflicts
[9] K. Taveter, and M. Meriste, “How can agents help in designing in Goal-oriented Requirements Engineering for Socio-technical
complex systems?”, Journal of Telecommunication, Electronic and Systems,” in Proceedings of the 16th International Conference on
Computer Engineering, vol. 9, no. 2-9, pp. 1-8, 2017. Software Technologies (ICSOFT 2021), SCITEPRESS, pp 333-341,
2021.
[10] C. M. Macal, “Everything you need to know about agent-based
modelling and simulation,” Journal of Simulation, vol. 10, no. 2, 144– [20] T. Peters, “Personality Traits Modeling of Operations Control Center
156, 2016. (OCC) Personnel and Vocational Implications,” Master’s Thesis.
Technical University of Berlin, 2015.
[11] K. Mooses, and K. Taveter, “Agent-Oriented Goal Models in
Developing Information Systems Supporting Physical Activity Among [21] T. Doce, J. Dias, R. Prada, and A. Paiva, “Creating individual agents
Adolescents: Literature Review and Expert Interviews,” Journal of through personality traits,” in International Conference on Intelligent
Medical Internet Research, vol. 23, no. 5, 2021. Virtual Agents, pp. 257-264, Springer, 2010.
[12] G. J. Read, P. M. Salmon, N. Goode, and M. G. Lenné, „A [22] S. Ahrndt, J. Fähndrich, and S. Albayrak, “Modelling of personality in
sociotechnical design toolkit for bridging the gap between systems‐ agents: From psychology to implementation,” in Proceedings of the 4th
based analyses and system design,“ Human Factors and Ergonomics International Workshop on Human-Agent Interaction Design and
in Manufacturing & Service Industries, vol. 28, No. 6, pp. 327-341, Models (HAIDM 2015), pp. 1-16, Smart Society Project, 2015.
2018. [23] J. K. Connor-Smith, and C. Flachsbart, “Relations between personality
[13] L. S. Sterling and K. Taveter, The Art of Agent-Oriented and coping: A meta-analysis,” Journal of Personality and Social
Modeling. Cambridge, MA: MIT Press, 2009. Psychology, vol. 93, no. 6, pp. 1080-1107, 2007.
[14] K. Taveter, H. Du, and M. N. Huhns, “Engineering societal information [24] I. Robertson, D. Bartram, and M. Callinan, “Personnel selection and
systems by agent-oriented modeling,” Journal of Ambient Intelligence assessment,” in P. Warr, Ed., Psychology at Work, pp. 100–152,
and Smart Environments, vol. 4, no. 3, pp. 227-252, 2014. Penguin Press, 2002.