=Paper=
{{Paper
|id=Vol-2422/paper23
|storemode=property
|title=Fuzzy Production Model for Managing Court Decisions in the Case of Theft
|pdfUrl=https://ceur-ws.org/Vol-2422/paper23.pdf
|volume=Vol-2422
|authors=Anna Bakurova,Mariia Pasichnyk,Elina Tereschenko,Yurii Filei
|dblpUrl=https://dblp.org/rec/conf/m3e2/BakurovaPTF19
}}
==Fuzzy Production Model for Managing Court Decisions in the Case of Theft==
https://ceur-ws.org/Vol-2422/paper23.pdf
284
Fuzzy Production Model for Managing Court Decisions
in the Case of Theft
Anna Bakurova[0000-0001-6986-3769], Mariia Pasichnyk[0000-0002-5179-4272],
Elina Tereschenko[0000-0001-6207-8071] and Yurii Filei[0000-0002-1264-2831]
Zaporizhzhya National Technical University, 64, Zhukovskoho Str., Zaporizhzhia, 69061,
Ukraine
abaka111060@gmail.com, mashali@ukr.net,
elinatereschenko@ukr.net, fileyyuriy@gmail.com
Abstract. The economic essence of the theft, as a crime against property, and its
connection to unemployment is revealed. The general model of the support
system making court decisions as fuzzy production system is developed. For the
case of theft (Article 185 of the Criminal Code of Ukraine), two variants of the
implementation of the fuzzy production system – the Mamdani and Sugeno
algorithms – are proposed. Incorporation of the developed model into the
“Electronic Court” system, which is a feature of the information society, is able
to increase the level of automation of judicial practice and prevent corruption.
Keywords: unemployment, theft, decision support system, court decision,
linguistic variable.
1 Introduction
In addition to the legal aspect, the concept of theft has an economic essence, since it is
a crime against property. Theft is defined as a set of actions committed by one or a
group of entities that provide for secret seizure or gainful possession of property, which
subsequently harms the economic activity of both natural and legal persons. Thefts are
the most frequent crimes committed in Ukraine - they account for more than 40% of
the total number of reported crimes. The dynamics of the thefts is shown in Table 1.
The data are taken from open sources, the website of the Prosecutor General’s Office
of Ukraine [1].
Table 1. The dynamics of the thefts in 2013-2018 years.
Year Total crimes Theft The percentage of theft from the total number of crimes
2013 563560 242769 43.07%
2014 529139 226833 42.86%
2015 565182 273756 48.43%
2016 592604 312172 52.67%
2017 523911 261282 49.87%
2018 487133 238492 48.95%
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The main factors of the theft spread in Ukraine are: decrease in the living standards
of the population as a result of the socio-economic crisis, changes in legislation on the
qualification of such a crime as theft, unemployment. About 65% of thefts at the time
of the crime commission were not taken in work and educational activities, more than
a third were previously tried [2].
Punishment appointing is rather complicated and multidimensional process.
According to the Art. 65 of the Criminal Code of Ukraine [3], the court imposes
punishment within the limits established in the sanction of the Special Part Article of
the Criminal Code, which provides for responsibility for the committed crime, in
accordance with the provisions of the General Part of the Criminal Code, taking into
account the gravity degree of the offense, the person of the offender the circumstances
that mitigate and burden the punishment. During choosing a sentence, the judge must
evaluate all elements of the crime and all the circumstances of its implementation in
order to determine the extent of liability of the defendant and the appointment of him a
co-sentence punishment. The degree of punishment, depending on the composition of
the crime is regulated by the rules of law, which allows formally determine the limits
of maximum and minimum penalty. In addition to the objective factors in this process,
there is also subjectivity, the so-called judicial oversight. The choice of the type of
punishment where the law provides for alternative sanctions remains for the judge.
Consequently, the weakly formalized part of the sentence remains the assessment of
the circumstances of committing a crime and the characteristics of the guilty person.
While judges do not require a detailed comment on the criteria for evaluation, the need
for a very motivated choice of punishment is clearly regulated. To unify the account of
mitigating and burdening circumstances and the guilty person it is natural to formalize
their assessments. The development of a general knowledge base for sentencing, with
all possible combinations of different circumstances, gives hope for similar sentences
in similar composition and circumstances of crimes.
The object of this study is the process of taking court decisions in case of theft. The
subject of the study determines the methods of collecting and analyzing the parameters
of real court decisions presented in the natural language.
The purpose of the article is to build a general decision support system (DSS) in
court as a fuzzy production system, as well as to conduct a cycle of experiments with a
developed DSS based on real case data from the Unified State Registry of Judicial
Decisions in Ukraine [4].
2 Problem Statement
Punishment appointing is rather complicated and multidimensional process. According
to the Art. 65 of the Criminal Code of Ukraine, the court imposes punishment within
the limits established in the sanction of the Special Part Article of the Criminal Code,
which provides for responsibility for the committed crime, in accordance with the
provisions of the General Part of the Criminal Code, taking into account: 1) the gravity
degree of the offense, 2) the person of the offender, 3) the circumstances that mitigate
and burden the punishment.
286
The legislative sanction of the article takes into account both quantitative indicators
of the relevant circumstances and qualitative ones.
In accordance to this, were chosen the following input variables.
The linguistic variable Severity, which characterizes the degree of gravity of the
offence, takes on the meaning of the term set {small, medium, large}. This variable
allows you to take into account the repetition of a crime, the existence of past
punishment, a collective crime, and so on.
The linguistic variable Personality characterizes the identity of the offender and
takes value with the term set {negative, neutral, positive}. It allows for taking into
account, for example, employment, availability of socially useful activities, description
from the place of residence, etc.
It should be noted, that according to Part 3 of Art. 66 of the Criminal Code of
Ukraine, if in any of the circumstances mitigating the punishment provided for in the
Article of the Special Part of the Criminal Code as a sign of a crime that affects his
qualification, the court can not once again take it into account when imposing a
punishment as such that mitigate the punishment [3]. There are eleven mitigate
circumstances. Two linguistic variables were chosen to assess the circumstances, that
mitigate the punishment. The linguistic variable Mitigation evaluates the possibility of
taking into account a judge of a certain number of realized circumstances. The linguistic
variable LM assesses the level of punishment mitigation by circumstances, that were
implemented.
Also, during constructing the algorithm of sentencing, we have taken the specified
in Art. 67 of the Criminal Code of Ukraine, burdening circumstances. Such
circumstances are determined by fourteen. When imposing a sentence, the court can
not recognize that it is burdened by other circumstances. If any of the circumstances
that burden a punishment is stipulated in the Article of the Special Part of the Criminal
Code as a sign of a crime affecting its qualification, the court can not re-consider it
when imposing a sentence as burden it [3]. Input variables to assess the circumstances
that impose a punishment on Burden and LB. The linguistic variable Burden gives an
assessment of the possibility of taking into account a certain number of realized
circumstances. The linguistic variable LB assesses the level of punishment burden by
the circumstances that were implemented.
The linguistic variable Lawyer characterizes the level of neutrality of the judge and
takes on the meaning of the term-set {soft, middle, hard}. We will assume that the judge
is fair in the level of “middle”. Introduction of additional terms will put the problem of
the adequacy of the sentence, the impact assessment of judges person.
The court may impose a measure of punishment, the constituent parts of which are
fines, restrictions of freedom and imprisonment (real and conditional), public works.
Assign the following output variables.
The output linguistic variable Fine determines the size of the fine. The output
linguistic variable Years determines the term of imprisonment. The output linguistic
variable RF (Restriction of freedom) determines the level of freedom restrictions. The
output linguistic variable Public Works determines the public works The output
linguistic variable Condition determines real and conditional imposition of punishment.
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For all output linguistic variables were chosen the term-set, which contain three
terms that characterize the implementation level {low, medium, high}.
The membership functions of the terms of input and output linguistic variables are
determined by experts. Value ranges are regulated by the relevant legislation separately
for each article. Thus, the general DSS model in court has the form:
(Fine, Years, RF, Public Works, Condition) =
=F(Severity, Personality, Mitigation, LM, Burden, LB, Lawyer), (1)
where F is the corresponding fuzzy output algorithm.
For the experiment, the authors selected art. 185 of the Criminal Code of Ukraine on
theft [3].
Different parts of Article Art. 185 of the Criminal Code of Ukraine on theft [3] have
different versions of sentences. Difficulty base of fuzzy production rules will be
determined by the content of certain articles.
3 Literature Review
The problem of limitation of the court decisions objectivity was raised in 1963 in
Clark’s work, “The Limits of Judicial Objectivity”, which pointed to the basic rule for
passing judgments: “Government of laws, and not of men” [5]. But achieving the
perfect implementation of such a rule is impossible for a number of reasons, one of
them is the uncertainty of many legal concepts. This is confirmed by the fact that the
European Court, in view of the versatility of the notion of “justice” in decisions of
national courts, does not define the criteria for a fair judicial discretion, but only sets
out its tentative decision taking into account the provisions of Art. 6 “Convention for
the Protection of Human Rights and Fundamental Freedoms” [6].
The approach to defining the fuzzy notion of “fair court” was proposed, in particular,
in the work of Yu. Tobot [7], where the notion of “impartiality” was adopted as the
criterion for a fair judicial discretion, indicating the same attitude of the court to the
different sides of the dispute, resolving it without giving preference to one of the parties,
that is, “neutrality” of the court. In this case, each judge has his own idea of justice
discretion. According to V. Ladychenko [8], justice is not so much theoretically
realized as it is intuitively perceived, sometimes with the subconscious understanding
of it: people seem to consider the correctness of one or another act of the judiciary on
the “internal scales” of justice.
The formalization of the decision-making process requires such scientific methods
that would provide the opportunity, on the input data collected during the investigation
and the pre-trial investigation, to propose the judge a version of the sentence, which is
formulated in the subject field language and is the same for all courts of the country.
In the monograph D. Dyadkin [9] developed an algorithmic approach to the
definition of a sentence according to the rules of law. The author advocates developing
a more formal approach in determining the extent of punishment and reducing the
proportion that is determined by the judge’s care. D. Dyadkin demonstrates, on the
example of assessing the social danger of crime, the possibility of a formal approach,
288
using fuzzy logical deduction. Another example is the work [10] devoted to the
development of a product model in making judgments based on the Mamdani algorithm
for the case of moderate causing of serious harm to health.
There is a sufficiently developed theory of fuzzy / linguistic models, which is
described in particular in [11]. Such fuzzy / linguistic models that are interpretable and
can also be learned from the data. Also, we note that methods of fuzzy mathematics are
widely used and are effective in formalizing the knowledge and experience of experts
in various fields of human activity, as demonstrated in publications [9-16].
Previously, by the authors of this article in [17], was constructed fuzzy production
system based on Sugeno’s algorithm. Work was based on the materials of criminal
sentences in relation to Part 1 of the Art. 185 of the Criminal Code of Ukraine. But
unresolved issues were the choice validity of the fuzzy output algorithm, the study of
the impact of different versions of sentences (according to various articles of the
Criminal Code of Ukraine) on the complexity of the production rules base.
4 Materials and methods
Legal documents are characterized by a certain structuring and precision of the
terminology that uses the terminology of the law. The style of legal documents is
marked by the language standardization and unification, the wide use of consistent
phrases, stencils, standard texts using. It can be argued that the good interpretation of
the fuzzy logical conclusion is determined by the well-established theory of the
semantics of the specialized language of the legal branch [11]. Despite this, not all legal
terms are subject to formalization, which justifies the choice of fuzzy mathematics
methods.
To construct the fuzzy production system, it is necessary to form a base of agreed
fuzzy production rules that contain formalized domain experts knowledge. The basic
formalism is the notion of a linguistic variable, which meaning can be the words and
phrases of the experts specialized natural language. The linguistic variable takes on the
term-set value, which elements are the terms given by a fuzzy set with a definite
membership function, as described in detail in fundamental labor [11].
During constructing the system and conducting experiments, the authors sought to
obtain an approximation of the known sentences values from the source [4], which
would allow the source data of the system to be used later as a reference, the basis for
sentencing a judge, common to all courts all over the country.
5 Experiments
As can be seen from formula (1), some output variables of common DSS model – Fine,
Years – take numerical values, so it is interesting to compare the possibilities of the
most common model of fuzzy logic output from Mamdani algorithm with fuzzy logic
output from Sugeno algorithm, which has a clear output the value of some function of
the input variables.
289
Consider the stages of the Mamdani algorithm and the Sugeno algorithm
implementations in the Fuzzy Logic Toolbox MatLab [18].
Without losing generalization for greater certainty, we will continue to consider the
process of making a judicial decision on the example of art. 185 of the Criminal Code
of Ukraine [3], consisting of five parts. To conduct an experiment, choose part 3 of this
article, whereby theft, which is associated with penetration into the home, other
premises or repository, or which has caused significant harm to the victim, is punishable
by imprisonment for a term of 3 to 6 years.
Thus, the general DSS model in court by the formula (1) is transformed for part three
of Art. 185 of the Criminal Code of Ukraine concerning theft [3] in the formula, which
has the form:
Years = F(Severity, Personality, Mitigation, LM, Burden, LB, Lawyer), (2)
where F is the corresponding fuzzy output algorithm.
Membership functions terms of input and output linguistic variables determined on
materials of sentences for criminal case under part 3 of Art. 185 of the Criminal Code
of Ukraine. The most successful were the trapezoidal term membership functions for
the input variables Severity, Lawyer, Mitigation, Personality, Burden, and triangular
for LB, LM.
Membership functions terms of input variables are presented in Fig. 1 and Table 2.
Fig. 1. The membership function of variable outputs on the Mamdani algorithm.
The membership functions of the terms of the output variables by the Mamdani
algorithm characterizing the years of imprisonment are presented on Fig. 2 and in
Table 3.
The membership functions of the output variable Years were built on the basis of
judicial practice, according to which it is known that the shortest term, which is
290
appointed according to Part 3 of Art. 185 of the Criminal Code of Ukraine, is one year.
The longest term – six years – is a very severe punishment that occurs in court sentences
very rarely.
Table 2. DSS input linguistic variables and their terms of membership functions.
Linguistic variables Terms designation and associated membership functions
d1 d2 d3
Severity
[0 0 0.6 1.2] [0.5 1 2 2.5] [1.8 2.4 3 3]
Disrepute Norm Good
Personality
[0 0 0.4] [0.14 0.46 0.54 0.86] [0.6 1 1.4]
m1 m2 m3
Mitigation
[0 0 3 4] [3 5 6 8] [7 8 11 11]
lm1 lm2 lm3
LM
[0 0 0.4] [0.1 0.5 0.9] [0.6 1 1.4]
b1 b2 b3
Burden
[0 0 4 6] [4 6 8 10] [8 10 14 14]
lb1 lb2 lb3
LB
[0 0 0.4] [0.1 0.5 0.9] [0.6 1 1.4]
Soft Middle Hard
Lawyer
[0 0 0.15 0.4] [0.05 0.4 0.6 0.85] [0.6 0.85 1 1]
Fig. 2. Surface response to output variable Years of input variables Severity, Mitigation.
Table 3. The output variables of DSS model.
Algorithm Variable y1 y2 y3
Mamdani Years [1 1 2] [1 2 3 4] [3 4 5 6]
Years [0.01 0 0.22 -0.144 -0.01 [0.01 0 0.22 -0.144 - [0.01 0 0.22 -0.144 -
Sugeno
(linear) 0.1 -0.09 2.9] 0.01 0.1 -0.09 2.9] 0.0 0 -0.0 2.9]
291
For an example of the DSS work result in Fig. 2 there is a response surface for the
Mamdani model for the output variable Years from the input variables Severity,
Mitigation.
For the Mamdani algorithm, such fuzzy production rules have been developed: IF
the degree of gravity of the offence = big AND the personality = negative AND the
mitigation circumstances = from 7 to 11 AND the burdening circumstances = from 8
to 14 AND the level of neutrality of the judge = middle AND the level of the burdening
circumstances = big AND the level of the mitigation circumstances = big THEN
punishment will be from 1 to 4.
In the case of the Sugeno algorithm, such fuzzy production rules have been
developed: IF the level of neutrality of the judge = “middle” THEN the punishment will
be y1, IF the level of neutrality of the judge = “soft” THEN the punishment will be y2,
IF the level of neutrality of the judge = “hard” THEN the punishment will be y3.
Table 4. Fuzzy Production Rules Rs, s = 1-28 for Fuzzy Output System by Mamdani
Algorithm.
Rs Severity Lawyer Burden MitigationPersonality LB LM Years
1 D1 Lb1 Lm3 Y1
2 D2 middle norm Y2
3 D2 soft M3 norm Lm3 Y2
4 D3 hard B3 disrepute Lb3 Y3
5 D3 middle B3 good Lb3 Y2
6 middle B3 M3 disrepute Lb1 Lm3 Y2
7 soft M3 Lm3 Y1
8 hard B3 Lb3 Y3
9 D3 middle B3 M3 disrepute Lb3 Lm3 Y2
10 D3 middle B3 M1 disrepute Lb3 Lm1 Y3
11 B3 Lb3 Y3
12 M3 Lm3 Y1
13 B3 M3 Lb3 Lm3 Y2
14 D1 Y1
15 D2 Y2
16 D3 Y3
17 soft Y1
18 Middle Y2
19 hard Y3
20 disrepute Y3
21 norm Y2
22 good Y1
23 B1 Lb1 Y1
24 B2 Lb2 Y2
25 B3 Lb3 Y3
26 M1 Lm1 Y3
27 M2 Lm2 Y2
28 M3 Lm3 Y1
In the case of the Mamdani algorithm, the knowledge base combines 28 production
292
rules (Table 4), three of which coincide with the basic rules of the Sugeno algorithm.
Due to such a number of rules, greater compliance with the non-linearity of the court
decision-making process is achieved.
6 Results
In Table 5 and Table 6 summarize the results of the experiment on the DSS developed
according to the sentences of six typical cases from the register of court decisions in
Ukraine [4].
Table 5. Input data to experiment.
Input
Data on offense by sentence Term value
variable
Case No. 207/2695/17
repeatedly, with penetration into the home; material damage in
Severity d3=2.5
the amount of 42059 UAH
Reccurence of crime Burden b3=5.8
Contrition Mitigation m3=7.65
not working, not married, previously sentenced Personality Disrepute=0.159
- LB lb3=0.635
- LM lm3=0.3
Case No. 206/4630/17
penetration into the home; material damage in the amount of
Severity d1=0.7
762.50 UAH
not been established by court Burden 0
not been established by court Mitigation 0
not working, the place of residence is characterized by a
Personality Norm=0.5
negative; abusing alcohol drinks, not convicted
- LB 0
- LM 0
Case No. 315/1155/17
penetration into another room, material damage for 290 UAH Severity d1=0.2
committing a crime in a state of intoxication Burden b3=1
sincere repentance and active assistance in the disclosure of the
Mitigation m3=1
crime
not married, not working, inclined to drink alcohol, inclined to
Personality Disrepute=0.4
persistent criminal activity, not convicted
- LB lb3=0.5
- LM lm3=0.3
Case No. 127/14282/16-k
repeatedly, with penetration into the home; material damage in
Severity d3=2.5
the amount of 4131.70 UAH
recidivism of a crime Burden b3=5.8
contrition Mitigation m3=7.65
not working, married, before convicted Personality Disrepute=0.2
- LB lb3=1
- LM lm3=0.1
Case No. 161/13758/17
293
Input
Data on offense by sentence Term value
variable
got into the territory of the house; material damage in the
Severity d1=0.7
amount of 516.15 UAH
committing a crime in a state of intoxication Burden b3=1
sincere repentance and active assistance in the disclosure of the
Mitigation m3=1
crime
not married, not working, before convicted Personality Disrepute=0.25
- LB lb3=0.5
- LM lm3=0.3
Case No. 311/2510/17
repeatedly, combined with penetration into the home; property
Severity d2=2.3
damage for the total amount of UAH 10800.28+ UAH 8527
not been established by court Burden 0
acknowledged guilty completely, repentantly Mitigation m3=1
not married, not working, before convicted Personality Norm=0.5
- LB 0
- LM lm3=0.3
Table 6. Comparison of judgments and decisions made by the DSS for the output variable
Years.
DSS re-
Devia-
Case No. Term of imprisonment by court sentence Algorithm commen-
tion
dation
Mamdani 3.25 0
207/2695/17 3 years and 3 months (3.25)
Sugeno 3.17 -0.08
Mamdani 3.25 +0.25
206/4630/17 3 years
Sugeno 2.9 -0.1
Mamdani 3.25 +0.25
315/1155/17 3 years
Sugeno 3 0
Mamdani 3.5 0
127/14282/16-k 3 years 6 months (3.5)
Sugeno 3.1 -0.4
4 years (with the establishment of probation 2 Mamdani 3.25 -0.75
161/13758/17
years) Sugeno 3 -1.0
4 years (Punishment with dismissal on the Mamdani 3.25 -0.75
basis of Art. 75 of the Criminal Code of
311/2510/17
Ukraine with the establishment of probation 3 Sugeno 2.75 -1.25
years)
In all cases, given in Table 5, it was considered that the decision is made by a fair
judge, that is, the input variable Lawyer takes the value Middle with the corresponding
value of the membership function 0.5.
The Mamdani algorithm for the first four cases presented in Table 6, has generated
the punishment that is either coincidental or more severe on 0.25 years than was
pronounced by a court sentence. The Sugeno algorithm in these cases showed an
absolute deviation of -0.4 to 0 years, with reducing the term of imprisonment.
For the last two cases from Table 6 both Mamdani and Sugeno algorithms generated
milder punishment compared with the term of imprisonment by court decision. This is
294
due to the influence of the more complex content of the sentence, which contains a
reference to other articles of the Criminal Code of Ukraine. Reduce or avoid this
discrepancy maybe the complication of the model (2) with the additional Condition
provided in the general model (1).
7 Discussion
When substantiating the choice of fuzzy output algorithm, it is necessary to take into
account possible errors in the generated solutions and the complexity of calculations by
the chosen algorithm. Similar questions were raised for an individual case of
approximation of the continuous function of one variable in the work [13].
At the level of the conducted experiment, both systems of fuzzy logic output
Mamdani and Sugeno showed the adequacy of the generated results of sentences
without the apparent advantage of one of the algorithms. But the linearity of the output
functions of the Sugeno algorithm provides a more simple setup of the fuzzy output
system and yields a gain from a computational point of view.
Both systems responded equally to the existence of additional conditions, which in
practice proved to be mitigating of the court desicion. This is confirmation of the need
to introduce qualitative, non-numeric parameters to the system’s input. The
introduction of such variables is more convenient in the system of fuzzy logic output
using the Mamdani algorithm.
The following steps of improving DSS in the courts are dictated by the need of
developing unified rules for initializing input variables, which will allow adjusting
fuzzy production models to obtain the fair verdict in cases involving the crime in several
parts of one article and / or several different articles of the Criminal Code of Ukraine.
8 Conclusions
The scientific novelty of the work determines the general model construction of the
decision support system in court as fuzzy production system, as well as a cycle of
experiments with the developed DSS on the basis of real data on convictions on cases
from the database of the Unified State Register of Court Solution in Ukraine.
The practical value of this work is that the use of fuzzy logic methods is potentially
productive to support fair court decisions, since it allows one to approach the
formalization of the notion of fair court decision.
It appears perspective to introduct such subsystem into the system of the Single
Judicial Information and Telecommunication System (SJITS) – “Electronic Court”,
which testing was started in 18 pilot courts of Ukraine from 04.06.2018, is considered.
This will increase the level of automation of routine moments of judicial practice, bring
the information society closer.
295
9 Acknowledgements
The work was carried out as the part of the research work “Mathematical modeling of
socio-economic processes and systems”, the registration number DB05038, at the
Department of System Analysis and Computational Mathematics of Zaporizhzhya
National Technical University.
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