Exploring the dynamics of providing cognition using a computational model of cognitive insomnia

IAES International Journal of Artificial Intelligence (IJ-AI)
Vol. 14, No. 1, February 2025, pp. 92~101
ISSN: 2252-8938, DOI: 10.11591/ijai.v14.i1.pp92-101  92
Journal homepage: http://ijai.iaescore.com
Exploring the dynamics of providing cognition using a
computational model of cognitive insomnia
Roqia Rateb1
, Mosleh M. Abualhaj2
, Adeeb Alsaaidah2
, Mohammad A. Alsharaiah3
,
Amaal Shorman4
, Nisrean Jaber Thalji5
1
Department of Computer science, Faculty of Information Technology, Al-Ahliyya Amman University, Amman, Jordan
2
Department of Networks and Cybersecurity, Faculty of Information Technology, Al-Ahliyya Amman University, Amman, Jordan
3
Department of Information Technology, King Abdullah II School of Information Technology, The University of Jordan, Amman, Jordan
4
Department of Information Technology, Al-Huson University College, Al-Balqa Applied University, As-Salt, Jordan
5
Faculty of Computer Studies, Arab Open University, Amman, Jordan
Article Info ABSTRACT
Article history:
Received Apr 2, 2024
Revised Aug 14, 2024
Accepted Aug 29, 2024
Insomnia is a common sleep-related neuropsychological disorder that can lead
to a range of problems, including cognitive deficits, emotional distress,
negative thoughts, and a sense of insufficient sleep. This study proposes a
providing computational dynamic cognitive model (PCDCM) insight into
providing cognitive mechanisms of insomnia and consequent cognitive
deficits. Since the support providing is significantly dynamic and it includes
substantial changes as demanding condition happen. From this perspective the
underlying model covers integrating of both coping strategies, provision
preferences and adaptation concepts. The model was found to produce
realistic behavior that could clarify conditions for providing support to handle
insomnia individuals, which was done by employing simulation experiments
under various negative events, personality resources, altruistic attitude and
personality attributes. Simulation results show that, a person with
bonadaptation and either problem focused or emotion focused coping can
provide different social support based on his personality resources, personality
attributes, and knowledge level, whereas a person with maladaptation
regardless the coping strategies cannot provide any type of social support.
Moreover, person with close tie tends to provide instrumental, emotional, and
companionship support than from weak tie. Finally, a mathematical analysis
was used to examine the possible equilibria of the model.
Keywords:
Cognitive science
Computational modeling
Insomnia
Intelligent system
Provision process
This is an open access article under the CC BY-SA license.
Corresponding Author:
Roqia Rateb
Department of Computer Science, Faculty of Information Technology, Al-Ahliyya Amman University
Amman, Jordan
Email: r.alshorman@ammanu.edu.jo
1. INTRODUCTION
Insomnia is a condition where a person experiences poor quality sleep, difficulty falling and staying
asleep, waking up during the night, or waking up earlier than intended [1]. These symptoms can result in
daytime problems such as decreased cognitive function, decreased productivity, fatigue, depression, irritability,
impaired decision-making, low motivation, and mood changes [2]. Researchers have become interested in the
cognitive-vulnerability models, which explain how insomnia can lead to mood dysregulation and cognitive
deficiencies [3]. If insomnia is the primary concern, a vicious cycle of insomnia-anxiety-insomnia can emerge,
leading to affective dysregulation, impulsivity, restlessness, excessive daytime sleepiness, decreased vigilance,
and cognitive decline over time [4].

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Exploring the dynamics of providing cognition using a computational model of cognitive … (Roqia Rateb)
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There are various theoretical and cognitive-computational models related to insomnia, including the
cognitive vulnerability model for insomnia induced mood disturbances (CVMIMD), the sleep-specific cognitive
vulnerability (SSCV), the behaviorally induced insufficient sleep syndrome with restricted and extended sleep
opportunity (BIISS-RESO), and the global cognitive vulnerability to insomnia (GCVI) [5], [6]. This paper
focuses on the development of a computational model to understand how social support networks (SSNs) can
aid in providing effective support to individuals suffering from insomnia. The paper delves into various cognitive
and theoretical models related to insomnia, including CVMIMD and the BIISS-RESO.
In the field of intelligence analysis, there is a growing interest in understanding how cognitive
capabilities are affected by collaborative models in order to generate high-quality plans, make effective
decisions, and develop shared awareness of insomnia [7], [8]. Two approaches have been used to study this:
social psychological research, which focuses on small groups of humans observed in a task context, and
computer simulation techniques, which can explore 'what-if' scenarios and manipulate variables that are
difficult to control in real-world situations [9], [10]. However, there are limitations to both approaches. Social
psychological research is limited to small groups with simple communication structures and tasks that may not
generalize to real-world situations, while computer simulations often lack psychologically-realistic cognitive
processing capabilities [11], [12]. Some researchers argue for the use of socio-cognitive simulations to address
these limitations.
Various factors can motivate individuals who provide social support when faced with the task of
assisting someone with insomnia [13], [14]. It can be a challenging experience for family members, spouses,
or friends who are offering support to someone with this condition [15], [16]. The cognitive motivational
relational theory by Lazarus and Folkman suggests that the way providers appraise the situation of insomnia
can determine whether they cope with anxiety by attempting to change the situation (problem-focused) or by
addressing the emotional consequences of the events (emotion-focused) [17], [18]. Although most providers
handle the task of helping someone with insomnia well, there are some who struggle [18], [19].
Numerous research studies explore the adverse effects that insomnia can have on individuals,
including emotional exhaustion, burden, and maladaptation [18], [19]. Providing support to someone with
insomnia is a highly dynamic process, and it requires significant resources to monitor effectively in real-world
settings. Each provider has various cognitive and physiological mechanisms that regulate how insomnia
impacts their health and well-being [19], [20]. While much effort has been devoted to understanding the
behavioral mechanisms of providing support, there has been limited attention given to a computational
modeling perspective on how providers can assist individuals with insomnia [20], [21].
In the current paper, we describe a computational model of insomnia cognition that is grounded in an
extensive body of work in the psychological literature. The model features the use of SSNs to model one
particular form of providing cognition, namely coping process, adaptation concepts, and support preferences, as
this has become one of the fundamental components for building a provider model that can give social support
of individuals with different condition during demanding events. The primary contribution of this paper is the
novel insight it provides into the role of SSNs in the context of insomnia. This is significant because existing
research predominantly focuses on the cognitive and behavioral aspects of insomnia without integrating a
computational perspective on social support. By introducing a computational model grounded in psychological
literature, this study addresses the gap in understanding how providers' cognitive and physiological mechanisms
interact when supporting someone with insomnia. This research not only enhances the theoretical framework
around insomnia and social support but also has practical implications for designing interventions that can better
assist individuals dealing with insomnia. The major issue addressed here is the lack of comprehensive models
that incorporate socio-cognitive simulations to realistically depict cognitive processing in support scenarios,
bridging the gap between theoretical research and real-world applications.
By the end of the introduction, readers will understand that this paper offers a computational approach
to studying insomnia and social support, why this approach is valuable, and how the argument will be
developed through the subsequent sections. The section 2 presents the essential principles of insomnia
provision behavior throughout demanding events. From this standpoint, the formulation and design of a formal
model is developed in section 3. In section 4, simulation traces are shown to demonstrate how this model meets
the expected outcomes in social network ties. Section 5 presents a detailed mathematical analysis to evaluate
the correctness of the proposed model. Finally, section 6 concludes this paper.
2. THE COMPREHENSIVE THEORETICAL BASIS ON COGNITIVE VULNERABILITY MODEL
FOR INSOMNIA
From a neurocognitive perspective, the pre frontal cortex (PFC), which is responsible for regulating
emotions and cognitive functions, undergoes significant development during neurodevelopment and
adolescence due to neuroplasticity. When hypnic tone is reduced due to poor sleep habits or socio-behavioral
and psychophysiological stressors, the activation of maladaptive processes in the PFC is proposed as a potential

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neurocognitive mechanism underlying the affective consequences of insomnia and inefficient sleep in general,
according to [20], [21]. The body of psycho-behavioral and neurocognitive research that explains the exact
mechanisms that link insomnia and negative mood is limited. However, subjective experiences of sleep
insufficiency and dysregulated mood have a stronger association than objective findings from
polysomnography or sleep electroencephalography data [22]. This suggests that psychological factors that
interfere with sleep efficiency may play significant roles in explaining the relationship between sleep and mood
[23]. Some of these cognitive vulnerability factors associated with insomnia are now recognized as mistaken
beliefs, cognitive biases, and thought patterns that increase the likelihood of individuals developing
psychopathology.
2.1. Sleep specific cognitive vulnerability
In some cases, mistaken beliefs and attitudes are specifically related to sleep problems. In such cases,
the dysfunctional beliefs and attitudes about sleep (DBAS) scale is used to assess the distressing worries related
to insomnia. Harvey's cognitive model explains how these dysfunctional beliefs and attitudes can affect
insomnia complaints. According to this model, insomniacs often worry about poor sleep and its consequences
during the day. These negative thoughts can trigger a bias towards monitoring sleep-related threat cues, which
leads to over-monitoring of these cues. Previous studies have shown a strong link between DBAS and poor
sleep, which plays a significant role in disturbances in sleep perception and sleep safety behaviors such as
napping, according to Harvey's model [23].
2.2. Behaviorally induced insufficient sleep syndrome with restricted and extended sleep opportunity
Patients commonly complain of difficulty sleeping at night and difficulty waking up in the morning,
which is referred to as a specific condition [24]. People with initial or maintenance insomnia tend to have
shorter habitual sleep episodes than the norm for their age group, as confirmed by their medical history, sleep
log, or actigraphy. These patients often experience sleep inertia in the morning and excessive daytime
sleepiness for at least three months before seeking medical help [25]. However, they tend to sleep for longer
periods on weekends or during vacations. Objective measurements of sleep efficiency using polysomnography
indicate that it is generally below 80%, and the mean time it takes to fall asleep at night is longer than 45
minutes. Additionally, patients report experiencing repeated awakenings during the night.
2.3. Global cognitive vulnerability to insomnia
Global cognitive vulnerability is characterized by dysfunctional beliefs and attitudes that are not
specific to any particular behavior or experience. According to Beck's cognitive model, negative attitudes and
biases towards oneself, the world, and the future can develop from early life traumas and complicated past
experiences, resulting in maladaptive schemas that trigger cognitive vulnerabilities and negative tendencies,
leading to depression later in life. Several studies have found strong links between sleep problems, particularly
insomnia, and negatively toned cognitive constructs associated with anxiety, depression, and hopelessness.
Personalized treatment approaches are necessary to address the various types of insomnia, which include
adjustment, drug-induced, comorbid, onset, middle, late, conditioned, behavioral, idiopathic, paradoxical, and
sleep hygiene insomnia. Insomnia patients can be categorized into neutral or clinical types based on the severity
of their sleep difficulties. Gross's model of emotion explains how emotions are generated and evolve over time
through a process that involves attention, appraisals, multi-faceted emotional responses, and feedback to
modulate the current situation continually [26].
The model shows how emotions are dynamic and suggests strategies for regulating them, such as
situation selection, modification, attention deployment, cognitive change, and response modulation. Previous
studies mentioned in the text lacked discussion on causal relationships, which may lead to misinterpretation of
findings. Thus, there is a need for a new approach to conceptualize theories and hypotheses to draw data models
for testing mediating relationships between independent variables and outcomes in retrospective studies and to
suggest research strategies for prospective studies on insomnia. This study aims to address this gap in the
literature by proposing and validating a novel computational cognitive model for insomnia based on previous
models. The computational cognitive model explains how cognitive processes and interactions can cause
annoyance and distress reactions, leading to the development or maintenance of insomnia. The study also
demonstrates the insomnia numerical model through multi-mediatory (causality) modeling approaches.
3. METHOD
A computational cognitive model is used as techniques in this paper. Thus, in cognitive science,
models can generally be categorized into three types: computational, mathematical, and verbal-conceptual.
Computational models use algorithmic descriptions to detail processes. Mathematical models employ

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mathematical equations to depict relationships between variables. Verbal-conceptual models describe entities,
relationships, and processes using natural language. Regardless of the type, each model can be seen as a theory
explaining the phenomena it aims to represent.
While all these model types have their roles, this paper primarily focuses on computational modeling,
particularly those based on computational cognitive architectures. This emphasis is due to the current promise
of computational modeling, which offers unmatched flexibility and expressive power. It provides various
modeling techniques and methodologies and supports practical applications of cognitive theories.
Mathematical models are often considered a subset of computational models since they can usually be
implemented computationally. Computational models are mainly process-based theories, focusing on how
human performance occurs, the psychological mechanisms and processes involved, and the specific ways in
which these operate. It is also possible to develop theories of the same phenomena through "product theories,"
which provide a precise functional account without committing to specific psychological mechanisms or
processes. A providing computational dynamic cognitive for insomnia (PCDCI) model was evolved from [27]
model, as mentioned in the previous section (analysis of cognitive dynamics in appraisal and coping strategy),
computational properties for the provision insomnia model can be specified. These computational properties
are illustrated to simulate the individual’s reaction in coping when exposed to the depression, as well as the
possible consequences of that action as shown in Figure 1.
Figure 1. Variables and its relationships that involved in the providing modeling for insomina
3.1. Depression
The depression (de) denotes the degree of stress experienced by a person based on negative events
(Nv) and his or her personal resources (Pr), which is controlled by the proportion factor (𝜂𝑖).
𝑑𝑒(𝑡) = 𝜂𝑖. 𝑁𝑣(𝑡) + (1 − 𝜂𝑖). (𝑁𝑣(𝑡)). (1 − 𝑃𝑟( 𝑡)) (1)
3.2. Challenge and threat
Challenge (Ch) is positively related with personality attributes (Pa), while negatively with the
intensity of stress through the proportional factor (𝛽𝑐).
𝑇ℎ(𝑡) = (𝐼𝑒(𝑡). (1 − 𝑃𝑎(𝑡)) (2)

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𝐶ℎ(𝑡) = 𝛽𝑐. 𝑃𝑎(𝑡) + (1 − 𝛽𝑐). 𝑃𝑎(𝑡). (1 − 𝐼𝑒(𝑡)) (3)
3.3. Problem and emotional-focused coping
The presence of threat generates emotional-focused coping (Ef) level, whereas the presence of
challenge generates problem-focused coping (Pf), which delivers a positive effect.
𝐸𝑓(𝑡) = 𝑇ℎ(𝑡). (1 − 𝐶ℎ(𝑡)) (4)
𝑃𝑓(𝑡) = 𝐶ℎ(𝑡). (1 − 𝑇ℎ(𝑡)) (5)
3.4. Burden level and self–esteem
A combination of personal resources (Pr) and personality attributes (Pa) triggers the self-esteem (Sf)
through proportional contribution (𝛼𝑠). Self-esteem (Sf), and second proportional contribution (𝜎𝑏) between
emotion-focused coping (Ef) and long-term emotional exhaustion (Eh).
𝑆𝑓(𝑡) = 𝛼𝑠 𝑃𝑟( 𝑡) + (1 − 𝛼𝑠). 𝑃𝑎(𝑡) (6)
𝐵𝑙(𝑡) = (1 − [𝛾𝑏. 𝑃𝑓 ⥂⥂ (𝑡) + (1 − 𝛾𝑏). (𝑆𝑓(𝑡)]). (𝜎𝑏. 𝐸ℎ(𝑡) + (1 − 𝜎𝑏). 𝐸𝑓(𝑡)) (7)
3.5. Maladaptation, bonadaptation, and emotional exhaustion
The maladaptation (Ma) is calculated using the combination of burden level and emotional-focused
coping. In the case of bonadaptation, it is determined by measuring the burden level and problem-focused
coping. Parameters 𝜔𝑚 and 𝜂𝑏, provide a proportional contribution factor in respective relationships.
𝑀𝑎(𝑡) = 𝜔𝑚. 𝐸𝑓(𝑡) + (1 − 𝜔𝑚). 𝐵𝑙(𝑡) (8)
𝐵𝑎(𝑡) = 𝜂𝑏. 𝑃𝑓(𝑡) + (1 − 𝜂𝑏). (1 − 𝐵𝑙(𝑡)) (9)
𝐸ℎ(𝑡 + 𝛥𝑡) = 𝐸ℎ(𝑡) + 𝜓𝑒 . [(𝑀𝑎 (𝑡) − 𝐸ℎ(𝑡)). (1 − 𝐸ℎ(𝑡)). 𝐸ℎ (𝑡)]. 𝛥𝑡 (10)
3.6. Empathy capability, altrustic motivation, and willingness to help
The presence of both positive personality (Pa) attributes and self-esteem (Sf) trigger the empathy
capability (Ec) through a proportional factor (𝜆𝑒). A combination of altruistic attitude (Al) and empathy
capability (Ec) determine the level of the altruistic motivation (Am) by a proportional factor ( 𝛽𝑚).
𝐸𝑐(𝑡) = 𝜆𝑒. 𝑃𝑎(𝑡) + (1 − 𝜆𝑒). (𝑆𝑓(𝑡)) (11)
𝐴𝑚(𝑡) = 𝛽𝑚. 𝐴𝑙(𝑡) + (1 − 𝛽𝑚). (𝐸𝑐(𝑡)) (12)
𝑊ℎ(𝑡) = 𝜆𝑤. 𝐵𝑎(𝑡) + (1 − 𝜆𝑤). [1 − (1 − 𝐴𝑚(𝑡)). 𝑀𝑎(𝑡)] (13)
3.7. Support provision preference (informational, instrumental, emotional, and companionship)
All support provision preferences require willingness to help (Wh) in the proposed model, and with
its additional attributes.
𝐼𝑝(𝑡) = (𝑊𝑖1. 𝐾𝑙(𝑡) + 𝑊𝑖2𝐶𝑚(𝑡)). 𝑊ℎ(𝑡) (14)
𝐸𝑝(𝑡) = 𝑊ℎ(𝑡). [𝜓𝑒𝐶𝑚(𝑡) + (1 − 𝜓𝑒). (𝑊𝑒1. 𝐴𝑔(𝑡) + 𝑊𝑒2. 𝑃𝑐(𝑡))] (15)
𝑁𝑝(𝑡) = 𝑊ℎ(𝑡). [𝜏𝑛𝐶𝑚(𝑡) + (1 − 𝜓𝑒). (𝑊𝑛1. 𝐴𝑔(𝑡) + 𝑊𝑛2. 𝑃𝑐(𝑡) + 𝑊𝑛3 𝑃𝑟( 𝑡))] (16)
𝑀𝑖𝑎,𝑏(𝑡) = ∑𝑠𝑖𝑚(𝑅𝑖(𝑡), 𝑃𝑠(𝑡))/𝑛𝑚 (17)
𝐶𝑝(𝑡) = 𝑊ℎ(𝑡). [𝜇𝑐𝐶𝑚(𝑡) + (1 − 𝜇𝑐). (𝑊𝑐1. 𝐸𝑣(𝑡) + 𝑊𝑐2. 𝑃𝑐(𝑡) + 𝑊𝑐3. 𝑀𝑖 (
𝑎,𝑏 𝑡))] (18)
3.8. Committed support and total of provision support
The long-term concept committed support (Cm) can be employed to measure willingness to help (Wh)
by flexibility rates (𝜎𝑐). The total of support related to 𝑛𝑠 number of supports is called total provided support (Ts).

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𝐶𝑚(𝑡 + 𝛥𝑡) = 𝐶𝑚(𝑡) + 𝜎𝑐 . [(𝑊ℎ ⥂ (𝑡) − 𝐶𝑚(𝑡)). (1 − 𝐶𝑚(𝑡)). 𝐶𝑚 (𝑡)]. 𝛥𝑡 (19)
𝑇𝑠(𝑡) = (𝐼𝑝(𝑡) + 𝑁𝑝(𝑡) + 𝐸𝑝(𝑡) + 𝐶𝑝(𝑡))/ ∑ 𝑛𝑠 (20)
4. RESULTS AND DISCUSSION
Many simulations have been performed to discover interesting patterns among insomnia provider’s
support tie preference behaviors. Some anticipated patterns can be discovered with variations in individual and
inter-personal attributes. This paper deals with three fictional individual conditions as shown in Table 1
exposed to different set of initial attributes. Table 1 summarizes the values of these profiles.
Based on this, there are five provision scenarios for Insomnia are presented: an individual likely to
provide informational support (A), an individual likely to provide emotional support (B), an individual likely
to provide instrumental support (C), an individual likely to provide companionship support (D), and an
individual is not providing any support (E). The duration of our simulation was initialized at 1,000 time points
under these flexibility and proportional settings; ∆t=0.2,𝛾𝑏=𝜂𝑖=𝛽𝑐=𝛼𝑠=𝜎𝑏=𝜔𝑚=⥂ 𝜓 ⥂𝑠=𝜆𝑒=𝜎𝑐=𝜆𝑤=0.5. For
this simulation, these individuals experience high negative events throughout the simulation time. Several
systematic experiments were conducted to obtain the most appropriate parameter settings for this model as
summarized in Table 2.
Table 1. Individuals profiles
Provider support preference individuals Profiles
Pr Nv Al Pm Pc Kl Pa Ag Ev
A 0.3 0.2 0.9 0.3 0.2 0.9 0.9 0.1 0.2
B 0.1 0.9 0.9 0.2 0.9 0.1 0.9 0.9 0.1
C 0.9 1.0 0.8 0.1 0.9 0.2 0.9 0.8 0.2
D 1.0 0.1 0.9 0.9 0.8 0.3 0.7 0.1 0.9
E 0.1 0.9 0.1 0.5 0.2 0.1 0.4 0.3 0.2
Table 2. Comparsion of results
Provison support of insomnia Results Previous work
Informational supports Low negative events [26]–[28]
Emotional supports Emotion-focused coping [29]–[31]
Companionship supports Mutual interest with recipient [32]–[34]
Instrumental supports Problem-focused coping [35]–[37]
4.1. Simulation trace for insomnia provision preferences
‒ Case # 1: Informational insomnia supports (limited personal resources and low negative events):
The personality attributes for individual A are high and also high knowledge level about the problem,
therefore he/she will cope with the incoming negative events through provided informational support as shown
in Figure 2(a). Moreover, it is shows that individual A developed better coping skills through the selection of
emotion-focused coping since he/she has insufficient resources and high negative events and in last threat faced
as primary stressors. Similar findings can be found in [26]–[28].
‒ Case # 2: Emotional insomnia supports (limited personal resources and highly negative events):
Normally, an individual with a high agreeableness personality (individual B) with perceived close tie
is high, prefers to provide emotional support as a mechanism to help recipient as shown in Figure 2(b). Similar
findings can be found in [29]–[31]. In addition, individual C provides also emotional support but less than
individual B since he/she has lower value for perceived close tie.
‒ Case # 3: Companionship insomnia supports (highly personal resources and low negative events):
A person with high with extraversion personality (individual D), tends to provide companionship
support as a coping process see Figure 3(a). These results are in line with existing literature as in [32]–[34]. In
this simulation trace, it is observable that individual C also provide companionship support but lesser than
individual D since he/she has lower mutual interest with recipient situation.
‒ Case # 4: Instrumental insomnia supports (highly personal resources and highly negative events)
In this simulation, individual C provide the highest value of instrumental support. One of the factors
can be used to explain this simulation is the increasing level of provider’s personal resources. Since he/she has
the higher personal resources compared with other individuals A, B, and E as shown in Figure 3(b). For
example, several studies reported that providers who were satisfied with helping others used more problem-
focused coping [35], [36].

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(a) (b)
Figure 2. Insomnia provided support: (a) informarional and (b) emotional preferences
(a)
(b)
Figure 3. Insomnia provided support: (a) companionship and (b) instrumental preferences
4.2. Mathematical analysis
The determination of the model’s equilibria is based on the mathematical formal analysis as mentioned
in [35], [36]. The equilibria describe the condition in which the values for the variables that experienced stable
situations. Theoretically, this total to almost 33
=27 possible equilibria. As the number of possible combinations
is enormous, developing a complete classification of equilibria is rather difficult. However, the analysis can be
pursued further for some typical cases. It must be noted that for each distinguished case more information is
available regarding the equilibrium values of the other variables based on the additional non-dynamic
equations.

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‒ Case #1: Eh= Ma  Cm= Wh
In this case, from (7), this case is equivalent to: 𝐵𝑙 = (1 − [𝛾𝑏. 𝑃𝑓 + (1 − 𝛾𝑏). 𝑆𝑓 ⥂⥂]). (𝜎𝑏. 𝑀𝑎 + (1 − 𝜎𝑏). 𝐸𝑓 ⥂).
Assuming 𝛾𝑏 and 𝜎𝑏 equal to 0.5, therefore; 𝐵𝑙 = (1 − [𝑃𝑓 + 𝑆𝑓 ⥂⥂]). (𝑀𝑎 + 𝐸𝑓 ⥂). From (8), this case gives;
𝑀𝑎 = 𝜔𝑚. 𝐸𝑓 + (1 − 𝜔𝑚). [(1 − [𝑃𝑓 + 𝑆𝑓 ⥂⥂]). (𝑀𝑎 + 𝐸𝑓 ⥂)]. Rearrange; 𝑃𝑓 = 1 − 𝑆𝑓 − (𝑀𝑎 − 𝐸𝑓)/(𝑀𝑎 + 𝐸𝑓)
and 𝜔𝑚,1. Consider (9), the equilibria point is 𝐵𝑎 = 1 − 𝑆𝑓 − (𝑀𝑎 − 𝐸𝑓)/(𝑀𝑎 + 𝐸𝑓) + (1 − 𝐵𝑙).
‒ Case #2: Cm =1
Assuming 𝑊𝑖1and 𝑊𝑖2 are nonzero, thus (14) provides equilibria point of; 𝐼𝑝 = 𝐾𝑙. 𝑊ℎ. Using the
same principle for (15), the effect of the stability point can be summarized as; 𝐸𝑝 = 𝑊ℎ. [𝐴𝑔 + 𝑃𝑐].
‒ Case #3: Eh =1
In this case, from (7), this case is equivalent to: 𝐵𝑙 = (1 − [𝛾𝑏. 𝑃𝑓 + (1 − 𝛾𝑏). 𝑆𝑓 ⥂⥂]). (𝜎𝑏 + (1 − 𝜎𝑏). 𝐸𝑓 ⥂).
If 𝛾𝑏 and 𝜎𝑏 are nonzero nor one, then 𝐵𝑙 = (1 − [𝑃𝑓 + 𝑆𝑓 ⥂⥂]). (𝐸𝑓 ⥂). Rearrange this, 𝐵𝑙 /𝐸𝑓 = (1 − [𝑃𝑓 + 𝑆𝑓 ⥂⥂]).
5. CONCLUSION
The insights gained from this study have three important implications. First, this research enhances
the theoretical framework surrounding insomnia and social support by integrating computational modeling
with psychological literature. It provides a new perspective on how SSN function in the context of insomnia.
Second, the model can be used to develop assistive technologies and interventions aimed at improving support
for individuals with insomnia. For example, assistive agents or applications could be designed to provide
real-time support and coping strategies based on the model’s findings. Third, the findings can inform policies
and training programs for healthcare providers, family members, and other caregivers, emphasizing the
importance of understanding cognitive and emotional processes in providing effective support to individuals
with insomnia. To build upon the findings of this study, future research should conduct empirical studies to
validate the model’s predictions and simulations against real-world data. While this paper presents a
comprehensive computational model for understanding insomnia and social support, several limitations must
be acknowledged:the model is primarily based on theoretical constructs and simulation data, which may not
fully capture the complexity of real-world scenarios. Furthermore, using generated simulation traces, the model
has been verified against a number of properties describing emerging patterns put forward in the literature. The
resulting model can be useful to understand how certain concepts in a societal level (for example; personality
attributes) may influence social providers while coping with insomnia. In addition to this, it could be used as a
mechanism to develop assistive agents that are capable to support individuals when they are facing
sleeplessness during a social support process. In conclusion, while the presented computational model offers
valuable insights into the interplay between insomnia and social support, addressing its limitations through
future research will enhance its applicability and impact, ultimately contributing to better support strategies for
those affected by insomnia.
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BIOGRAPHIES OF AUTHORS
Dr. Roqia Rateb has received her Ph.D. in Computer Science (Artificial
Intelligence/Data Science) from University Utara Malaysia (UUM) in 2020. Her master has
completed from Yarmouk university in Computer Information Systems (Data Mining) with
Excellent. She succeeded as the first of the batch and her ranking became on Honor’s List of the
University in all years of the study. Moreover, she has received her bachelor in Computer
Science form Jordan University of Science and Technology with Excellent. She is currently an
Assistant Professor at Department of Computer Science in Al-Ahliyya Amman University
(AAU), Amman, Jordan. Her research interests are modelling dynamics of (multi) agent in
practical application areas, such as social simulation, agents and cognitive robotics, problem
optimization, high-performance computing, cognitive simulation and modelling, and
configuration algorithm. In addition, her latest publication achieved a Certificate of Award for
the Best Special Session. She can be contacted at email: r.alshorman@ammanu.edu.jo.

101
Prof. Mosleh M. Abualhaj is a senior lecturer in Al-Ahliyya Amman University.
He received his first degree in Computer Science from Philadelphia University, Jordan, in 2004,
master degree in Computer Information System from the Arab Academy for Banking and
Financial Sciences, Jordan in 2007, and Ph.D. in Multimedia Networks Protocols from
Universiti Sains Malaysia in 2011. His research area of interest includes VoIP, congestion
control, and cybersecurity data mining and optimization. He can be contacted at email:
m.abualhaj@ammanu.edu.jo.
Dr. Adeeb Alsaaidah received the bachelor’s degree in computer engineering from
the Faculty of Engineering, Al-Balqa Applied University, the master’s degree in networking and
computer security from NYIT University, and the Ph.D. degree in computer network from
USIM, Malaysia. He is currently an Assistant Professor in network and cybersecurity
Department at Al-Ahliyya Amman University (AAU). His research interests include network
performance, multimedia networks, network quality of service (QoS), the IoT, network
modeling and simulation, network security, and cloud security. He can be contacted at email:
a.alsaaidah@ammanu.edu.jo.
Dr. Mohammad A. Alsharaiah is a Department Head of Computer Science at
Information Technology Faculty – Ahliyya Amman University He received a Ph.D. degree in
Computer Science and Information Systems from Lincoln University, Christchurch, New
Zealand in Dec. 2018. His research interest includes data science and artificial intelligence fields
such as machine learning, deep learning, data mining, big data, bioinformatics and modeling
complex systems, business intelligence, and pattern recognition. He can be contacted at email:
mohdsharaiah@gmail.com.
Dr. Amaal Shorman holds a Ph.D. in Computer Science from The University of
Jordan in 2022. Additionally, she has a Master's degree from New Mexico State University,
USA in 2012, and a Bachelor's degree from Jordan University of Science and Technology,
Jordan in 2006. She has served as a teaching and research assistant at New Mexico State
University. She has also held the position of Assistant Professor at Jadara University in Jordan.
Currently, she is an Assistant Professor at Al-Balqa Applied University, Jordan. Her research
interests in artificial intelligence, machine learning, data science, internet of things, security, and
cybersecurity. She has contributed significantly to the field. She has been awarded the Academic
Excellence Scholarship for having the highest GPA in the Ph.D. program at The University of
Jordan and the Top Tawjihi Student at Brigade Level Scholarship for achieving the highest
Tawjihi average score at Al Mazar brigade level. Her publications in esteemed journals and
conference proceedings reflect her expertise in developing innovative solutions in computer
science. For further inquiries regarding her research or collaborations. She can be contacted at
email: amal.shorman@bau.edu.jo.
Dr. Nisrean Jaber Thalji is an accomplished individual in the field of Computer
Science. She received her Doctorate degree in Computer Science, specializing in Artificial
Intelligence and Data Science, from the prestigious University Malaysia Perlis (UNIMAP),
located in Perlis, Malaysia, with an excellent academic record. Prior to that, she completed her
Master’s degree in Computer Science at Yarmouk University in Jordan, Irbid, where she
achieved outstanding academic performance and ranked first among her colleagues. She holds
a Bachelor’s degree in Computer Science from the same institution, Yarmouk University, with
a commendable academic standing. With a strong educational background, she is currently
serving as an Assistance Professor at Jadara University. Her diverse expertise spans across
various domains, including artificial intelligence, machine learning, deep learning, algorithm
engineering, swarm intelligence, and natural language processing. Her research interests
continue to thrive in these cutting-edge areas, further solidifying her reputation as a dedicated
scholar and researcher in the field. She can be contacted at email: n_thalji@aou.edu.jo or
nnthalji1980@gmail.com.

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