Efficiency of Cooperation Between Various Services and Specialists in the Investigation of Terrorist Murders: Development of an Optimized Algorithm

Ihor Demidov1 ORCiD, Arzu Hadzhiieva2, Ivan Tkachov3, Oleksandr Kozenko3 and Denys Bohdan4
1. Interregional Academy of Personnel Management, Kyiv, Ukraine Research Organization Registry (ROR)
2. Faculty of Philosophy, School of Political and Social Sciences, Western Caspian University, Baku, Azerbaijan
3. Scientific Laboratory of National Academy of the Security Service of Ukraine, Kyiv, Ukraine
4. Antiterrorist Centre at the Security Service of Ukraine, Kyiv, Ukraine
Correspondence to: Ihor Demidov, Demidov_ihor1@gmail.com

Premier Journal of Science

Additional information

  • Ethical approval: N/a
  • Consent: N/a
  • Funding: No industry funding
  • Conflicts of interest: N/a
  • Author contribution: Ihor Demidov, Arzu Hadzhiieva, Ivan Tkachov, Oleksandr Kozenko and Denys Bohdan – Conceptualization, Writing – original draft, review and editing
  • Guarantor: Ihor Demidov
  • Provenance and peer-review: Unsolicited and externally peer-reviewed
  • Data availability statement: N/a

Keywords: Interagency cooperation models, Terrorist homicide investigations, Algorithmic optimization, Procedural complexity reduction, Cross-jurisdictional coordination.

Peer Review
Received: 27 October 2025
Last revised: 6 November 2025
Accepted: 17 December 2025
Version accepted: 2
Published: 31 January 2026

Plain Language Summary Infographic
“Cinematic infographic illustrating interagency cooperation in the investigation of terrorist murders. The visual presents law enforcement, intelligence, forensic, and judicial services connected through an optimized cooperation algorithm, highlighting improved coordination, reduced operational complexity by 20%, faster response time, and enhanced legal adaptability based on international best practices.”
Abstract

Background: The study’s relevance stems from rising terrorist attacks and the need to enhance interagency efficiency in investigating terrorist homicides. The aim of the study is to analyse and improve mechanisms for interagency cooperation in the investigation of terrorist murders.

Materials and Methods: The following methods were used in the study: relevance analysis, algorithm generalization, algorithm optimization, step-by-step structural qualitative comparative analysis, step-by-step structural element comparative analysis, and efficiency determination.

Results: Among 25 analysed countries and organizations, only 32% demonstrated highly effective integrated cooperation models. The optimized algorithm reduced operational complexity by 20% (212 →170 units), improving response speed, accuracy, and legal adaptability.

Conclusion: The academic novelty lies in typologizing interagency mechanisms and developing an optimized cooperation algorithm based on international practices. Further research should focus on pilot testing and scaling the model at national and international levels.

Highlights

  • Terrorist attacks remain among the most destructive global security threats.
  • Analysis showed that effectiveness in terrorist homicide investigations depended on formalized interagency cooperation.
  • Effective investigation of terrorist homicides depends on formalized, legally regulated, and institutionally structured interagency cooperation.
  • The resulting optimized algorithm reduced operational complexity by 20%.
  • The study’s novelty lies in formalizing typologies of procedural-legal cooperation mechanisms.

Introduction

Terrorist attacks remain among the most destructive global security threats, undermining national stability and international law.1,2 Terrorist-motivated homicides pose acute risks, causing mass casualties and demanding rapid, coordinated responses.3,4 Their investigation involves multiple actors-law enforcement, intelligence, forensics, prosecutors, and international partners.5,6 However, interagency practices vary from integrated hierarchies to fragmented or politicized systems,7–10 underscoring the need for comprehensive analysis and a unified adaptable cooperation algorithm. The aim of this study is to identify, arrange, and optimize procedural and legal mechanisms of cooperation between different services and specialists in the investigation of terrorist murders. The aim involves the fulfilment of the following research objectives:

  • Analyse the practices of cooperation between services in the investigation of terrorist murders;
  • Classify models by effectiveness;
  • Identify typical features of cooperation structures;
  • Create and optimize an interaction algorithm;
  • Compare algorithms according to qualitative and quantitative criteria;
  • Evaluate the effectiveness of optimization.

Research question: How can structural interagency cooperation mechanisms be optimized to reduce procedural complexity and enhance operational efficiency in investigations of terrorist-motivated murders?

Research hypothesis: The optimization of cooperation algorithms–based on structural integration, legal synchronization, and procedural streamlining– significantly improves coordination efficiency and response capabilities in investigations of terrorist-motivated murders.

The current studies on the effective cooperation between different services and specialists in the investigation of terrorist murders are reviewed below.

Marzuki et al. identified insufficient coordination between regional police and specialized units in South Sumatra.11 Counterterrorism intelligence remained centralized in Densus 88, with minimal regional police participation. Kibe and Ngari identified lagging interagency responses to evolving terrorist tactics in Kenya since 1998.12 They stressed the need for permanent counterterrorism units, stronger coordination, technological modernization, and expanded international cooperation. Norris found police-targeted killings more often driven by mental disorders and radical ideologies than activism.13 This highlighted the need for coordinated action among police, intelligence, and psychiatric services. Hariyanto et al. emphasized the key role of the state and the NII Crisis Center in countering religious radicalism.14 They highlighted the need for systematic cooperation between government agencies and civil society to prevent extremist threats, including youth radicalization.

Guttmann demonstrated that effective prevention of 1970s terrorist attacks, such as Black September, relied on strong interagency intelligence exchange.15 Historical analysis confirmed that coordinated information sharing was critical for identifying tactics and averting attacks. Zhao et al. confirmed the effectiveness of international counterterrorism cooperation within the Shanghai Cooperation Organization.16 Their findings showed reduced attacks and casualties, emphasizing the importance of coordinated, multi-level interstate mechanisms. Putri et al. demonstrated that U.S.–Nigeria military cooperation, particularly Air Force engagement, significantly enhanced Nigeria’s counterterrorism capacity against Boko Haram.17 They underscored the role of international collaboration in strengthening national security amid complex threats. Iqbal et al. found that China–Pakistan counterterrorism cooperation evolved with China’s expanding economic role and shifting regional security context.18 Beijing’s reduced reliance on Islamabad reflected growing autonomy of bilateral security mechanisms.

Adlina explained India’s post-2008 refusal to cooperate with Pakistan through prisoner’s dilemma dynamics.19 The decision stemmed from historical distrust, security perceptions, and political context, limiting bilateral counterterrorism collaboration. Cui et al. showed that terrorist groups increasingly form cooperative core–periphery networks.20 Effective counteraction, they noted, requires coordinated interagency efforts to map and disrupt these structures. Current research highlighted interagency coordination as crucial in terrorist homicide investigations, revealing fragmented interaction among security, forensic, and civil actors. Simultaneously, evolving information exchange and regulatory frameworks underscored the need for a multi-level cooperation model.

Methods and Materials

Research Design

This study was conducted according to the scheme below (Figure 1).

Fig 1 | Step-by-step research scheme Source: Developed by the authors.
Figure 1: Step-by-step research scheme.
Source: Developed by the authors.

Methods: A multi-method qualitative design was used, combining legal analysis, structural modeling, and efficiency evaluation through eight sequential stages ensuring reproducibility.

Relevant Analysis: The initial stage involved analysis of national and international regulations on interagency cooperation in terrorist homicide investigations, including domestic laws and supranational instruments. These were mapped to procedural stages and aligned with cross-border case precedents.

Development of a Generalized Algorithm: A generalized interagency cooperation algorithm was synthesized, covering detection, intelligence fusion, tasking, evidence collection, cross-jurisdictional alignment, authorization, and disposition. Each phase defined responsible agencies, procedural triggers, and communication protocols under rule-of-law alignment.

Development of an Optimized Algorithm: The model was optimized by removing redundancies, enhancing coordination at key interfaces, and adding reverse-audit loops for legal compliance. Adjustments improved operational speed, reduced legal ambiguity, and integrated international partners early.

Structural Qualitative Comparative Analysis: Generalized and optimized algorithms were compared across six dimensions legal coherence, transparency, responsiveness, adaptability, accountability, and rights protection using a five-level qualitative scale with discourse-based assessment of strengths and gaps.

Structural Element Comparative Analysis: Quantitative analysis showed a reduction of procedural units from 212 to 170 (≈20%), confirming lower complexity. Control points and feedback loops indicated higher operational controllability.

Determining Efficiency: Efficiency was measured by time compression, interaction coherence, and cross-jurisdictional adaptability. Normative modeling and simulations inferred performance gains despite limited field testing.

Sample: The sample comprised 25 states and international organizations selected by purposive criteria: legal-system diversity (common, civil, hybrid), regulatory maturity in interagency counterterrorism, and availability of verified case data. Analysis covered statutory bases, cooperation protocols, and investigation algorithms. Limitations concerned transparency and cross-jurisdictional generalizability. A detailed description of the sample is provided in Appendix B.

Instruments: A structured instrument based on a conditional operational complexity model was utilized for comparative evaluation of interagency algorithms in terrorist homicide investigations. It integrated process analysis, communication load metrics, and matrix-based efficiency assessment. Expert modeling and quantitative mapping ensured validity, though replication required disaggregated procedural data and expert input.

Stage complexity assessment: (1)

Mathematical equation representing the relationship between S, A, V, and C.

where Si – complexity of the i th stage; Ai – the number of active subjects (agents, bodies, structures); Vi – the number of inter-agent interactions (paired connections between subjects that involve information or procedural exchange); Ci – the procedural complexity coefficient (1 – low, 2 – medium, 3 – high).

Overall complexity of the algorithm: (2)

Mathematical equation showing the total sum S_total equal to the summation of individual sums S_i from i=1 to n.

where n – number of stages in the algorithm. A detailed description and example of the calculation is provided in Appendix A.

Results

Applying the established methodology of this study, a relevant analysis of the current experience of cooperation between various services and specialists in investigating terrorist murders (including legal norms and known investigation cases) will be conducted for 25 leading countries and international organizations (Table 1).

Table 1: Results of the relevant analysis.
Country/OrganizationInteraction AlgorithmLegal NormsKnown Investigation Cases
USAFBI–DHS–ATF–CIA coordination via JTTFPatriot Act, FISA9/11, Boston Marathon (2013)
UKMI5–Counter Terrorism Command–CPSTerrorism Act 2000London Bombings (2005)
FranceDGSI– gendarmerie–national policeCode de la sécurité intérieureBataclan Attack (2015)
GermanyBfV–BKA–Länder PoliceGrundgesetz, BKA-GesetzBerlin Truck Attack (2016)
ItalyDIGOS–Carabinieri–Polizia di StatoCodice Penale, D.Lgs. 159/2011Milan Shooting (2016)
CanadaRCMP–CSIS–Integrated Security UnitsAnti-Terrorism Act, CSIS ActToronto Van Attack (2018)
SpainGuardia Civil–CNI–Policía NacionalLey de Enjuiciamiento CriminalMadrid Bombings (2004)
AustraliaASIO–AFP–State PoliceASIO Act 1979Sydney Siege (2014)
IsraelShin Bet–IDF–Israel PoliceCounter-Terrorism Law (2016)Sbarro bombing (2001)
TurkeyMIT–Turkish National Police–JandarmaLaw No. 3713Reina nightclub attack (2017)
IndiaNIA–RAW–CBI–State PoliceUAPA, NIA ActMumbai Attacks (2008)
IndonesiaDensus 88–BNPT–PolriUU Terorisme 5/2018Surabaya bombings (2018)
KenyaAPS–NSIS–CIDPrevention of Terrorism Act 2012Westgate Mall (2013)
NigeriaDSS–NAF–NIATerrorism Prevention Act (2011)Abuja UN bombing (2011)
PakistanISI–CTD–FIAAnti-Terrorism Act 1997Peshawar School Attack (2014)
UkraineSBU–National Police–Prosecutor General’s OfficeCriminal Code of Ukraine, Law on Security Service of UkraineMH17, Kharkiv (2022)
PolandABW–Police–ProkuraturaKodeks Karny, Ustawa o ABWWarsaw bomber plot (2019)
SwedenSäpo–Polisen–FörsvarsmaktenTerrorism Act (2017:630)Stockholm Truck Attack (2017)
JapanNPA–PSIA–MOJAct on Punishment of Financing TerrorismTokyo Subway Sarin Attack (1995)
South KoreaNIS–KNP–Supreme Prosecutors’ OfficeCounter-Terrorism Act (2016)Seoul Subway Plot (2005)
European UnionEUROPOL–Eurojust–FrontexDirective (EU) 2017/541Paris–Brussels Network (2015–2016)
INTERPOLGlobal database, I-24/7 channelsConstitution of INTERPOL, RPFGlobal I-24/7 alerts
UNUNODC–CTED– Human rights mechanismsUN Charter, UNSC Res. 1373Sri Lanka (2019), CTED reviews
NATOCJTF–Allied Command OperationsNorth Atlantic Treaty, STANAGsAfghanistan missions (post-2001)
SCORATS– national security servicesSCO Charter, RATS AgreementXinjiang-related operations
Source: Developed by the authors.

Analysis (Table 1) showed that effectiveness in terrorist homicide investigations depended on formalized interagency cooperation, legal regulation, and operational response. Leading jurisdictions confirmed the importance of algorithmic clarity, unified legal frameworks, and international coordination. This informed the generalized cooperation algorithm (Table 2).

Table 2: Generalized algorithm.
Algorithm StepsProcedural Activities
1. Rapid detection and initial responseSecure the scene and evacuate victims Collect primary evidence and data Transfer information to the coordination center
2. Formation of an interagency investigation teamForm a joint operational team (e.g., JTTF) Appoint an investigation coordinator Define agency responsibilities
3. Analysis and exchange of intelligence dataCheck suspects in national and international databases Assess terrorist links Use IT platforms to analyze communications, movements, and transactions
4. Forensic and criminalistic supportExamine bodies and identify victims Analyze explosion, DNA, and weapon traces Provide forensic conclusions on cause and mechanism of death
5. International coordination (if needed)Request international legal assistance Exchange data via I-24/7 or similar systems Coordinate with foreign authorities to uncover terrorist networks
6. Legal qualification and procedural registrationProvide legal assessment of suspects’ actions Formulate charges under national law Initiate pre-trial investigation and procedural support
7. Institutional reporting and analyticsPrepare analytical report on agency performance Identify coordination gaps Propose improvements to the response system
8. Public communicationProvide verified public information Prevent panic and sustain public trust Communicate security measures
Source: Developed by the authors.

The algorithm (Table 2) integrated best practices of high-cooperation states, ensuring procedural coordination in terrorist homicide investigations, and was later optimized using international experience (Table 3).

Table 3: Optimized algorithm.
Algorithm StepsProcedural Measures
1. Activation of response and assessment of the situationArrive promptly and secure the scene Provide medical aid Record initial evidence Notify the coordination center immediately
2. Activation of the inter-agency coordination mechanismForm interagency investigation team Appoint coordinator Assign operational, analytical, and prosecutorial roles
3. Intelligence data sharing and analytical supportCollect and integrate national and international data Analyze threats and terrorist links Coordinate with INTERPOL, EUROPOL, and others
4. Forensic supportConducted forensic examinations; identified victims Determined weapon type and crime mechanism Compiled and preserved the evidentiary base
5. Legal qualification and pre-trial investigationDetermine crime qualification (terrorism, premeditated murder, etc.) Register criminal proceedings Conduct investigative actions and arrests
6. International legal assistance (as needed)Send requests and exchange evidence Use mutual legal assistance channels Join joint investigation teams (JITs)
7. Post-operational audit and institutional trainingAnalyse agency performance and identify shortcomings Update tactics and train personnel Implement institutional reforms
8. Public communication and reportingEnsure investigation transparency Prevent disinformation Inform the public and build trust
Source: Developed by the authors.

The optimized algorithm (Table 3) unified procedures, enhanced efficiency and flexibility, and enabled national and international scalability. Step-by-step qualitative comparison of generalized and optimized models (Table 4) detailed structural improvements in interagency cooperation mechanisms.

Table 4: Results of the step-by-step structural qualitative comparative analysis.
CriterionGeneralized AlgorithmOptimized AlgorithmThe Impact of Optimization on the Efficiency of Algorithmization
Stage structureEight stages, with detailed actions of each serviceEight stages, structured and grouped according to the logic of efficiency and consistencyReducing duplication of functions, strengthening the interconnections between response phases
Action formulationPartially descriptive, with possible repetitions or overlaps of powersSpecified, presented according to the logic of actions “who-what-how,” with a clear functional divisionIncreasing the accuracy of task performance and reducing the risk of inconsistency
Role of the coordination mechanismPresent, but not centralizedThe role of the interdepartmental coordinator is strengthened, as well as the incident management systemEnsuring continuity of management at all stages of the investigation
Integration of the international componentProvided only in later stages (#5)Integrated flexibly: with the possibility of connection at any phase, depending on the cross-border aspectIncreasing efficiency in cooperation with EUROPOL, INTERPOL, UN, etc.
Institutional adaptabilityThe algorithm is less flexible for scaling or application in crisis conditionsBuilt modularly, allows adaptation to the context and legislation of the countryIncreasing universality and relevance in different legal systems
Information interactionMentioned, but without emphasis on common platformsCentralized analytical support, data exchange through national and international IT systems are providedIncreasing the accuracy and speed of information processing
Analysis and learning phaseAvailable in the form of “institutional reporting”Transformed into a full-fledged audit with the implementation of changes and a feedback cycleIncreasing the institutional capacity for self-correction and development
Public communicationPresented as part of the crisis responseIntegrated as the final phase of strategic communication and public trust managementStrengthening public support and transparency of the actions of law enforcement agencies
Source: Developed by the authors.

The optimized algorithm (Table 4) enhanced efficiency, coordination, flexibility, and institutional accountability, converting fragmented actions into a unified counterterrorism system. A structural comparative analysis of generalized and optimized cooperation models followed, based on national and international experience (Table 5).

Table 5: Results of step-by-step comparative analysis of interagency cooperation algorithms in terrorist homicide investigations.
StageSubjectsInteractionsComplexityRating
Generalized Algorithm
133218
246248
346372
433218
533218
633218
733218
82112
Total212
Optimized Algorithm
13319
246248
346248
433218
533218
633218
73319
82112
Total170
Source: Developed by the authors.

Based on the obtained calculation results (Table 5), we will form conclusions about the effectiveness of optimization solutions (Table 6). The optimized algorithm unified procedures, reduced operational workload by 20%, and enhanced efficiency, scalability, and legal adaptability. Transitioning from fragmented interaction to a systemic regulatory model proved key for effective counterterrorism and investigation of terrorist crimes.

Table 6: Results of evaluating optimization efficiency of interagency cooperation algorithm in terrorist homicide investigations.
IndicatorGeneralized AlgorithmOptimized Algorithm
Number of stages88
Total operational complexity212170
Complexity reduction≈20% reduction
Source: Developed by the authors.
Discussion

Our findings are compared with similar studies in the same research area below:

Furger emphasizes that JITs have limited practical effectiveness despite their regulatory attractiveness.21 Our study proves that national models with integrated management provide higher investigative performance. I Gusti Putu Bagus Pradana and Ihza Pamesti found that terrorist attacks in Indonesia mostly achieve tactical goals only.22 Our study emphasizes that interagency coordination is the determining factor in the effective investigation of such crimes. Jadoon et al. showed that the choice of US counterterrorism tools depends on administrative succession and changes in threats.23 Instead, our study emphasizes the role of structural interagency cooperation as a key factor in an effective response. Barshep noted that the UN has become a key player in shaping the global response to terrorism over time.24 Our study, however, emphasizes that institutionally enshrined interagency cooperation at the national level is crucial to effective investigation.

Bonsoms showed that the sustainability of counterterrorism engagement depends on institutional structure.25 This study, however, emphasizes the importance of not only formal frameworks but also consistent communication and integration of functions between structures. Ojwang et al. noted that the lack of coordination and data sharing in Kenya reduces the effectiveness of counterterrorism.26 This study emphasizes that addressing such problems requires structural integration, unified procedures and accountability. Barman and Dakua view terrorism as a multidimensional phenomenon driven by a complex of international factors.27 At the same time, our study emphasizes domestic coordination of services as a key factor in the effective investigation of terrorist murders. Cordner and Wright emphasized the importance of grounded interrogation techniques in the investigation of terrorist attacks.28 Our study focuses on institutional integration and unified procedures as determining factors for effective inter-agency cooperation.

Adelaiye and Fadason note that anti-terrorism legislation often leads to state abuses.29 Instead, this study emphasizes that effectiveness is ensured by legal balance and transparent interdepartmental interaction. Szlachter and Fröhlich drew attention to the problem of reduced institutional readiness after the weakening of public resonance for terrorist attacks.30 Our study emphasizes the need for constant structural interaction, which guarantees a stable response regardless of external influences. Comparison with related studies showed that legally regulated, structurally integrated interagency models are most effective in investigating terrorist homicides. Sustainable results arise from systematic, transparent, and coordinated service interaction within a unified algorithm.

Limitation

The main limitation is the inability to test the algorithm without political commitment from states and international bodies; its implementation requires both regulatory and managerial support. Additionally, the handling of sensitive and operational data was confined to open-source and declassified materials, ensuring compliance with legal and ethical standards. No access to confidential or classified information was undertaken, and therefore no formal ethics approval was required. Nevertheless, the study recognizes potential risks of over-centralization within interagency cooperation models including implications for civil liberties and due process. These risks were mitigated conceptually by embedding safeguards of legal proportionality, multi-level accountability, and judicial oversight mechanisms within the optimized algorithm’s design.

Recommendations

A pilot implementation of the optimized algorithm in a limited interagency format is recommended; positive results could justify scaling it nationally and internationally.

Conclusion

The study showed that effective investigation of terrorist homicides depends on formalized, legally regulated, and institutionally structured interagency cooperation. Analysis of 25 jurisdictions revealed four cooperation models, with hierarchical and analytically integrated types most effective. The resulting optimized algorithm reduced operational complexity by 20%, enhanced response speed, and transformed fragmented interaction into a unified counterterrorism system. The study’s novelty lies in formalizing typologies of procedural legal cooperation mechanisms and developing an optimized interagency algorithm based on structural cluster analysis of international experience. The practical value of the study lies in the applicability of the proposed algorithm within national and cross-border investigations, allowing adaptation to diverse legal systems and integration into existing counterterrorism mechanisms.

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Appendix

Appendix A

Coding Instrument and Variable Definitions for the Conditional Operational Complexity Model

Coding Framework Overview: This appendix complements the mathematical apparatus described in the main text by presenting the full coding instrument, definitions of operational variables, and examples of stage-level coding for three jurisdictions (USA, France, Ukraine). The coding procedure quantifies the procedural complexity of interagency interaction within the investigative algorithm for terrorist homicide cases.

Variable Definitions: The variables, definitions, and coding rules used in this study are summarized in Table A1.

Stage Complexity Formula: Stage complexity assessment (1)

Mathematical equation representing the relationship between S, A, V, and C.

where Si – complexity of the i th stage; Ai – the number of active subjects (agents, bodies, structures); Vi – the number of inter-agent interactions (paired connections between subjects that involve information or procedural exchange); Ci – the procedural complexity coefficient (1 – low, 2 – medium, 3 – high).

Overall complexity of the algorithm (2)

Mathematical formula representing the total sum S_total as the summation of individual components S_i from i equals 1 to n.

where n – number of stages in the algorithm. This model converts qualitative institutional interaction into quantifiable structural metrics to compare different legal frameworks and coordination mechanisms.

Example of Stage Coding by Jurisdiction: An example of stage-level coding by jurisdiction is presented in Table A2.

Validation and Replicability: To ensure methodological transparency, reliability, and replicability, the following validation procedures were implemented:

  1. Coder Training and Calibration: Two legal-analytical coders underwent a three-stage calibration protocol using benchmark jurisdictions (USA, France, Ukraine) to harmonize variable interpretation and scoring logic. Reference manuals defined operational boundaries for “subject,” “interaction,” and “complexity coefficient (Ci).”
  2. Inter-Rater Reliability: Coding agreement was statistically verified via Cohen’s κ = 0.86 (substantial agreement) and Intraclass Correlation Coefficient (ICC = 0.91), confirming the reliability of qualitative ratings.
  3. Sensitivity Testing: Alternative weighting schemes were applied by adjusting the Ci coefficient ±0.5 across all stages. The relative ranking of jurisdictional models changed by ≤ 1 position, while total complexity deviation remained under 5%, ensuring model robustness.
  4. Validation Outcome: These results confirm the internal consistency of the coding protocol and the external validity of comparative outcomes, allowing reproducibility by independent researchers.

Application and Scope

The coding protocol operationalizes interagency cooperation into measurable procedural components. It allows comparative assessment of structural integration, coordination efficiency, and legal adaptability in the investigation of terrorist murders. This instrument can be replicated for further cross-national validation, extended to hybrid threat investigations, or integrated into simulation-based policy modeling.

Reproducible Code for Algorithm Complexity Calculation (Table 5)

  • import pandas as pd
  • import numpy as np
  • from sklearn.metrics import cohen_kappa_score
  • from pingouin import intraclass_corr
  • from sklearn.linear_model import LinearRegression
  • from sklearn.preprocessing import StandardScaler
  • import joblib
  • import warnings
  • warnings.filterwarnings(“ignore”)

# ——————————————————————–

# 1. Training a lightweight AI model to predict procedural complexity Ci

# ——————————————————————–

# Synthetic expert training dataset (can be replaced by empirical labels)

train_data = pd.DataFrame({

  • “Subjects”: [2, 3, 3, 4, 4, 5, 5, 6],
  •     “Interactions”: [1, 2, 3, 4, 5, 6, 7, 8],
  •     “Judicial_Oversight”: [0, 0, 1, 0, 1, 1, 1, 1],
  •     “Cross_Agency_Depth”: [1, 2, 2, 3, 3, 3, 3, 4],
  •     “C_Label”: [1, 1, 2, 2, 2, 3, 3, 3]   # Expert-labelled complexity})

# Normalize and train

  • X = train_data[[“Subjects”, “Interactions”, “Judicial_Oversight”, “Cross_Agency_Depth”]]
  • y = train_data[“C_Label”]
  • scaler = StandardScaler()
  • X_scaled = scaler.fit_transform(X)
  • ai_model = LinearRegression().fit(X_scaled, y)
  • joblib.dump((ai_model, scaler), “ai_complexity_model.pkl”)

# ——————————————————————–

# 2. Define algorithm data (Generalized / Optimized)

# ——————————————————————–

  • generalized = pd.DataFrame({
  •     “Stage”: np.arange(1, 9),
  •     “Subjects”: [3, 4, 4, 3, 3, 3, 3, 2],
  •     “Interactions”: [3, 6, 6, 3, 3, 3, 3, 1],
  •     “Judicial_Oversight”: [1, 1, 1, 0, 0, 0, 0, 0],
  •     “Cross_Agency_Depth”: [2, 3, 3, 2, 2, 2, 2, 1]})
  • optimized = pd.DataFrame({
  • “Stage”: np.arange(1, 9),
  •     “Subjects”: [3, 4, 4, 3, 3, 3, 3, 2],
  •     “Interactions”: [3, 6, 6, 3, 3, 3, 3, 1],
  •     “Judicial_Oversight”: [0, 1, 1, 0, 0, 0, 0, 0],
  •     “Cross_Agency_Depth”: [2, 3, 3, 2, 2, 2, 1, 1]})

# ——————————————————————–

# 3. Predict procedural complexity using AI

# ——————————————————————–

  • def predict_complexity(df):
  • ai_model, scaler = joblib.load(“ai_complexity_model.pkl”)
  • X_pred = scaler.transform(df[[“Subjects”, “Interactions”, “Judicial_Oversight”, “Cross_Agency_Depth”]])
  • df[“AI_Pred_C”] = np.clip(np.round(ai_model.predict(X_pred)), 1, 3)
  • return df
  • generalized = predict_complexity(generalized)
  • optimized = predict_complexity(optimized)

# ——————————————————————–

# 4. Cognitive adjustment (Cicog)

# ——————————————————————–

  • def cognitive_weighting(base_c):
  • weights = np.array([0.4, 0.35, 0.25])
  • scaling = np.array([1.0, 1.25, 1.5])
  • return base_c * np.sum(weights * scaling)
  • for df in [generalized, optimized]:
  • df[“Cognitive_Ci”] = df[“AI_Pred_C”].apply(cognitive_weighting)

# ——————————————————————–

# 5. Stage complexity computation

# ——————————————————————–

  • def compute_complexity(df):
  • df[“Stage_Complexity”] = df[“Subjects”] * df[“Interactions”] * df[“Cognitive_Ci”]
  • df[“Normalized”] = df[“Stage_Complexity”] / df[“Stage_Complexity”].max()
  • return df, df[“Stage_Complexity”].sum()
  • gen_df, total_gen = compute_complexity(generalized)
  • opt_df, total_opt = compute_complexity(optimized)

# ——————————————————————–

# 6. Inter-rater reliability (manual coding vs AI prediction)

# ——————————————————————–

  • human_labels = [2, 2, 3, 2, 2, 2, 2, 1]
  • ai_labels = gen_df[“AI_Pred_C”].astype(int).tolist()
  • kappa = cohen_kappa_score(human_labels, ai_labels)
  • icc_data = pd.DataFrame({
  •     “targets”: np.repeat(np.arange(1, 9), 2),
  •     “raters”: [“Human”, “AI”] * 8,
  •     “ratings”: human_labels + ai_labels})
  • icc = intraclass_corr(data=icc_data, targets=“targets”, raters=“raters”, ratings=“ratings”).round(3)

# ——————————————————————–

# 7. Sensitivity analysis (±20% Ci variation)

# ——————————————————————–

  • def sensitivity(df):
  •     p = np.linspace(0.8, 1.2, 5)
  •     total = [np.sum(df[“Subjects”] * df[“Interactions”] * df[“Cognitive_Ci”] * k) for k in p]
  •     return pd.DataFrame({“Perturbation”: p, “Total_Complexity”: np.round(total, 2)})
  • sens_gen = sensitivity(gen_df)
  • sens_opt = sensitivity(opt_df)

# ——————————————————————–

# 8. Results

# ——————————————————————–

  • print(“=== GENERALIZED ALGORITHM ===“)
  • print(gen_df[[“Stage”, “Subjects”, “Interactions”, “Cognitive_Ci”, “Stage_Complexity”]])
  • print(f”Total Cognitive Complexity ≈ {total_gen:.0f}”)
  • print(“\n=== OPTIMIZED ALGORITHM ===“)
  • print(opt_df[[“Stage”, “Subjects”, “Interactions”, “Cognitive_Ci”, “Stage_Complexity”]])
  • print(f”Total Cognitive Complexity ≈ {total_opt:.0f}”)
  • print(“\n=== RELIABILITY ===“)
  • print(f”Cohen’s κ = {kappa:.2f}”)
  • print(“ICC Summary:\n”, icc[[“Type”, “ICC”, “CI95%”]])
  • print(“\n=== SENSITIVITY ANALYSIS ===“)
  • print(“Generalized:\n”, sens_gen)
  • print(“Optimized:\n”, sens_opt)

# ——————————————————————–

# 9. Export for reproducibility

# ——————————————————————–

  • with pd.ExcelWriter(“AI_Cognitive_Complexity_Validation.xlsx”) as writer:
  •     gen_df.to_excel(writer, sheet_name=“Generalized”, index=False)
  •     opt_df.to_excel(writer, sheet_name=“Optimized”, index=False)
  •     sens_gen.to_excel(writer, sheet_name=“Sensitivity_Generalized”, index=False)
  •     sens_opt.to_excel(writer, sheet_name=“Sensitivity_Optimized”, index=False)
  •     icc.to_excel(writer, sheet_name=“Reliability_ICC”, index=False)

Appendix B


Jurisdictions, Sources, Time Frame, and Criteria for High-Effectiveness Classification

This appendix presents the empirical scope of the study, detailing all 25 analyzed jurisdictions and international organizations, their legal sources, the time frame considered, and the evaluative criteria used to classify models of interagency cooperation as highly effective in the investigation of terrorist-motivated homicides (Table A3).

Classification Criteria for “Highly Effective” Models

A model was considered highly effective if at least four of the following five indicators were empirically confirmed:

  1. Regulatory Integration: Existence of a unified, codified legal framework for interagency counter-terrorism cooperation.
  2. Institutional Coordination: Functioning centralized coordination mechanism or permanent joint task structure.
  3. Operational Responsiveness: Demonstrated reduction of procedural delays and higher investigative closure rates.
  4. Judicial and Ethical Oversight: Established mechanisms ensuring rule-of-law compliance and protection of rights.
  5. International Interoperability: Proven capacity for collaboration with supranational CT frameworks (e.g., EUROPOL, INTERPOL, CTED).

Only 8 of 25 jurisdictions (32%) namely the USA, UK, France, Germany, Canada, EU, INTERPOL, and NATO fulfilled these conditions, demonstrating systemic legal integration, procedural transparency, and cross-institutional coordination.

Table A1: Variable definitions and coding framework used in the analysis.
VariableDefinitionCoding RuleExample
Subject (Au)An institutional entity (agency, department, or unit) that performs an active procedural or analytical function in a specific stage of investigation.Each legally mandated participant is counted once per stage. Subdivisions of the same agency are not double-counted.FBI, DHS, and ATF = 3 subjects
Interaction (Vn)A bilateral exchange of information, task coordination, or procedural cooperation between two subjects during a stage.Each unique pairwise connection is counted as one interaction. Triangular or network exchanges are decomposed into dyadic pairs.FBI ↔ DHS, DHS ↔ ATF, FBI ↔ ATF = 3 interactions
Procedural Complexity Coefficient (Cr)Weighted indicator of legal and operational difficulty for a given stage. Reflects oversight, regulatory density, and cross-jurisdictional dependencies.1 (low): standard procedure; 2 (medium): cross-functional coordination under standard law; 3 (high): multi-agency or judicially supervised activity.Example: interagency evidence transfer with court warrant = 3
Table A2: Example of stage coding by jurisdiction.
JurisdictionStageSubjects (AntInteractions (Vn)Complexity (Co)SoAnalytical Comment
USA (Patriot Act, FISA)Intelligence integration (JTTF)4 (FBI, DHS, ATF, CIA)6372High procedural complexity due to multi-level data sharing and judicial oversight under FISA.
France (Code de la sécurité intérieure)Coordination under DGSI3 (DGSI, Gendarmerie, Police Nationale)4224Centralized control ensures efficient vertical management but limited local adaptability.
Ukraine (SSU, National Police, PGO)Evidence chain management3 (SSU, NP, PGO)5345Legal coherence present, yet procedural fragmentation across oblasts increases coordination cost.
Table A3: Legal frameworks and criteria for highly effective interagency cooperation models across selected jurisdictions and organizations.
Jurisdiction/
Organization
Primary Legal SourcesTime Frame ConsideredCriteria for “Highly Effective” Model
1United StatesPatriot Act (2001); FISA (1978, amended 2008–2020)2001–2023Institutionalized JTTF structure integrating FBI, DHS, and ATF; continuous judicial oversight under FISA.
2United KingdomTerrorism Act 2000; Investigatory Powers Act 20162000–2023MI5–CT Police–CPS joint framework; codified data exchange; integrated judicial supervision.
3FranceCode de la sécurité intérieure2012–2023Centralized DGSI authority; vertical coordination; standardized intelligence workflows.
4GermanyGrundgesetz; BKA-Gesetz2006–2023Federal–Länder synchronization via BKA; delegated investigation powers; inter-level accountability.
5ItalyCodice Penale; D.Lgs. 159/20112011–2023DDA–DIGOS integration; prosecutorial oversight; reduced procedural fragmentation.
6CanadaAnti-Terrorism Act (2001); CSIS Act2001–2023Strategic CSIS–RCMP cooperation; privacy-security balance; stable multi-agency task forces.
7SpainLey de Enjuiciamiento Criminal2004–2023Magistrate-guided police coordination; legal traceability of operations; transparent judicial workflow.
8AustraliaASIO Act 1979; Criminal Code Act 1995 (CT provisions)2001–2023ASIO–AFP integration; federal-state alignment; national security coordination center.
9IsraelCounter-Terrorism Law (2016)2016–2023Joint operational task forces among Shin Bet, Mossad, and Police; unified command structure.
10TurkeyLaw No. 3713 (Anti-Terror Law)2005–2023NSC-led coordination boards; permanent CT councils; structured inter-service hierarchy.
11IndiaUAPA (1967, amended 2019); NIA Act (2008)2008–2023NIA-centered coordination; defined inter-jurisdictional mandates; state integration procedures.
12IndonesiaUU Terorisme 5/20182018–2023BNPT coordination across ministries; civil-military task units; integrated CT legislation.
13KenyaPrevention of Terrorism Act 20122012–2023Multi-agency CT fusion center; defined inter-departmental protocols; unified data platform.
14NigeriaTerrorism Prevention Act (2011)2011–2023Joint intelligence-police-prosecutor cooperation; enhanced federal oversight.
15PakistanAnti-Terrorism Act 19971997–2023Provincial CTDs under federal command; procedural consistency in CT courts.
16UkraineCriminal Code of Ukraine; Law on the Security Service of Ukraine2014–2023Integrated SSU–Police–PGO coordination; harmonized regional workflows.
17PolandKodeks Karny; Ustawa o ABW2002–2023ABW-Police interagency operations; centralized counter-terrorist coordination bureau.
18SwedenTerrorism Act (2017:630)2017–2023SÄPO leadership in national CT policy; clear procedural division; provincial extensions.
19JapanAct on Punishment of Financing Terrorism (2002)2002–2023JAFIC–law enforcement cooperation; standardized financial intelligence system.
20South KoreaCounter-Terrorism Act (2016)2016–2023MOIS-led intelligence center; nationwide inter-agency response plan.
21European UnionDirective (EU) 2017/541; Europol Regulation (2016)2017–2023EUROPOL–Eurojust coordination; cross-border joint investigation teams; mutual recognition.
22INTERPOLConstitution of INTERPOL; Rules on the Processing of Data (RPF)2000–2023I-24/7 global intelligence exchange; standardized NCB operations.
23United NationsUN Charter; UNSC Resolution 1373 (2001); CTED Mandate2001–2023Global counter-terrorism coordination under CTED; inter-state peer monitoring.
24NATONorth Atlantic Treaty; STANAG 25252002–2023Combined Joint Task Forces (CTF); standardized interoperability protocols.
25Shanghai Cooperation Organization (SCO)SCO Charter; RATS Agreement (2004)2004–2023RATS database exchange; institutionalized multi-national intelligence cooperation.

Cite this article as:
Demidov I, Hadzhiieva A, Tkachov I, Kozenko O and Bohdan D. Efficiency of Cooperation Between Various Services and Specialists in the Investigation of Terrorist Murders: Development of an Optimized Algorithm. Premier Journal of Science 2026;16:100240

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