Horatiu MOGA
Maritime Univeristy of Constanta,
Constanta, Romania
horatiu.moga@gmail.com

Mircea BOSCOIANU
Transilvania University of Brasov, Faculty of Technological Engineering and Industrial Management, Brasov, Romania
boscoianu.mircea@yahoo.com

Rezumat

The research aims to extend the poliheuristic foreign policy paradigm as a predictive spread in the strategic analysis of cyber information. The extension of the poliheuristic paradigm calls for the analysis of the operational code of a decision maker as well as decisions based on prospect theory in order to assess the impact within the domestic political system of extreme policy consequences subject to the constraints of cyber attacks. The evaluation based on operational code analysis allowed establishing a hierarchy of objectives of a foreign policy decision-maker and the appeal to prospect theory opened the perspective of evaluating the consequences left by foreign policy at the domestic level with the capability of assessing risks and the opportunity to move towards strategic analysis of cyber information. This will allow us to assess the impact of cyber attacks as foreign policy constraints. This article has allowed us to take a significant step in the knowledge necessary to evaluate international relations, foreign policy analysis, geopolitics and security studies usable in strategic analysis of cyber intelligence through the new tool we propose entitled poliheuristic predictive strategic cyber intelligence analysis.

Key words: operational code analysis; prospect theory; poliheuristic paradigm; predictive strategic cyber intelligence analysis.

Introduction

Our article aims to present a new research option that is open to specific risk analysis, strategic intelligence analysis [1], foreign policy analysis based on the poliheuristic paradigm [2] and prospect theory [3]. This research focuses on the connection between the data explanation experience provided by cyber intelligence analysis and the theoretically assessed high-level risk of a state using poliheuristic foreign policy analysis and elements of prospect theory. The openness that data collected with specific elements of cyber intelligence analysis allow for the explanation of the behaviors that state or non-state actors have in the international arena using the vast experience of several decades of theoretical apparatuses in international relations theory, geopolitics or foreign policy analysis. Our research explains the various foreign policy behaviors that a state can have through the constraints to which cyberwarfare in general subjects it as an increasingly present international disruptive factor in the world political system. This research attempts to fill some gaps for which science has not offered explanations in the field of international relations of cyber warfare as an increasingly present phenomenon in our daily lives. Thus, the science of international relations and foreign policy analysis [4] as well as geopolitics [5] refers to works that analyze various aspects of cyber warfare or cyber politics (as an international system of the online environment) through extensions of classical theories or the proposal of new theories [6]. Therefore, this research is part of a long series of authors’ searches through which they try to join the general trend of explaining the phenomena of the online environment that transcend state borders and that can favor or disfavor the behaviors of state or non-state actors, their objectives and the results obtained by them. Our article starts by defining the main elements of the poliheuristic analysis of foreign policy focused on indicators of operational code analysis [7] and elements of prospect theory [8]. It then moves through the field of strategic cyber intelligence analysis with an opening towards the poliheuristic approach of prospect theory and the attempt to propose as a final conclusion Poliheuristic Predictive Strategic Cyber ​​Intelligence Analysis. Therefore, our research aims to use practical information about the Arab Spring phenomenon as an example, through which some Arab states were destroyed as states or others escaped the online actions on American social networks but also the field activities of American secret services [9]. Our research will justify, through an example on certain Arab states, how the prospect theory curve explains the decisions and consequences in the domestic political system it can have on the foreign policy of a state subject to fake news attacks in Arab societies.

Approach

This section touches on four larger subsections. These are: the poliheuristic paradigm of foreign policy, indicators of operational code analysis, elements of prospect theory in foreign policy analysis, elements of cyber intelligence analysis. This research is an improvement on a previous approach to poliheuristic foreign policy analysis with risk study [10] but which did not consider the introduction of cyberwarfare or cyberpolitics variables nor cyber intelligence analysis. As described above in this paper, this approach is an improvement of the specific elements of the poliheuristic paradigm of foreign policy with indicators of operational code analysis that define the belief system that foreign policy factors exercise in the decision-making process. These can lead, due to the online environment involved in the foreign policy decision-making process, to time constraints, information, lack of communication and perception of risks that a state actor must assume with fatal consequences for itself. The cited research in the fields of international relations and foreign policy [11], as well as geopolitics [12], addresses theories in general but does not focus on the specific study of strategic cyber intelligence analysis risk. In the research [13] the authors specify that the classical theories in the fields of international relations and foreign policy, as well as geopolitics, are not adequate for the study of online implications in international relations nor in the calculation of risk. The authors resort to a game theory approach to risk-based decision-making which here we consider limited due to the ability of a state or non-state actor to collect accurate numerical data at the international level. More precisely we consider it to be the rule-based decision-making approach such as the poliheuristic paradigm of foreign policy. In the international specialized literature, we found evidence of alternative research between risk-based decision making with game theory [14] and the rule-based one that is addressed in this article. Next, we will define the four subsections that we cited at the beginning of the approach section.

1. 1. Paradigma Polieuristica de Politica Externa

It is a synthesis approach to foreign policy analysis that focuses on the two-step study of decision-making by combining the cognitive and rational approaches. In the first stage of the analysis, the focus is on cognitive analysis and identifies foreign policy actors and their management mechanism based on “cognitive heuristics short-cuts” such as [15]: B01. “Focusing on short-term benefits rather than longer-term problems”; B02. “Preference over preference”; B03. “Locking on one alternative”; B04. “Wishful thinking”; B05. “Post-hoc rationalization”; B06. “Relying on the past”; B07. “Focusing on a narrow range of policy options rather than on a wide range of options”; B08. “Groupthink” B09. “Overconfidence; over-estimating one’s capabilities and underestimating one’s capabilities”; B10. “Ignoring critical information; denial and avoidance”; B11. “Focusing on only part of the decision problem”; B12. “Turf battles leading to suboptimal decisions”; B13. “Lack of tracking and auditing of prior decisions and plans”; B14. “Poliheuristic bias”; B15. “Shooting from the hip”; B16. “Polythink”; B17. “Group polarization effect”. After that, the poliheuristic analysis matrix is ​​defined. In the second, rational stage, the hierarchy of decisions is established based on the decision rules. The decision matrix is ​​a tabular system that evaluates the hierarchy of the actor’s behaviors in foreign policy versus the objectives it has. These are evaluated according to the rates of behaviors and the weights of objectives. The alternatives in the decision matrix are its columns and define the foreign policy behaviors. The dimensions of the decision matrix are its lines and define the objectives of the state actor in foreign policy. These are usually chosen according to variables of the domestic political system that are influenced or can influence foreign policy [16]. Implications are descriptions of the consequences that alternatives have on the dimensions. Implications are related to rates. Rates are values ​​from -10 to + 10 that the consequences of alternatives have on the dimensions. Dimensions that have values ​​from -10 to -1 are considered non-critical and are excluded from the decision matrix. The hierarchical organization of the dimensions is based on the non-compensatory principle and orders them based on weights that have values ​​from 1 to +10. The second stage of the poliheuristic paradigm establishes decision relationships between weights and rates to establish the hierarchy of alternatives-dimensions, i.e. policy-objective pairs.

1. 2. Elemente de Teoria Prospectului in Analiza de Politica Externa

It first emerged as a theory of economic decision [17] that is in opposition to rational decision-making and game theory, in the 1990s. It quickly spread as a psychological theory of political decision-making [18]. It focuses on the study of decision-making and risk-taking as opposed to risk aversion. It argues that a decision-maker is generally more focused on preserving momentum and minimizing losses than on gains in critical moments. The centerpiece of prospect theory is the value curve in the figure below (Fig. 1). It has three defining zones: the area in quadrant one defined between the horizontal axis and the thick dotted line is the risk-averse zone in which the player has guaranteed gains; the second zone is the neutral zone located at the intersection of the vertical value axis with the horizontal gain axis (in this zone the decision-maker preserves his quantity and avoids loss or gain); The third area is the most important for the study of the third quadrant in which the player wants to maintain at least the neutral state but fails due to the sunk costs that he pays in order to obtain the desired result. Due to these costs that are avoided by decision makers in prospect theory, assuming the loss is more important than ensuring the gain. Prospect theory is an approach similar to the poliheuristic paradigm [19] in which the decision is made in two stages in which the objectives, probabilities and utilities are defined in the first stage and the decisions are made in the second stage.

Fig.1. Prospect theory curve aproximation (Source: constructed by author)

1. 3. Indicators of Operational Code Analysis

Operational code indicators are defined as a tool of study in foreign policy analysis after 1948 [20]. Their approach called operational code analysis has been modified several times [21] being defined by two types of beliefs: philosophical beliefs (defining the inputs of the foreign policy decision maker) and instrumental beliefs (defining the outputs of the foreign policy decision maker). These indicators are as follows:

1. 1. 1. The Philosophical Beliefs in an Operational Code

1.1.The Nature of the Political Universe (P-1): ‘What is the “essential” nature of political life? Is the political universe essentially one of harmony or of conflict? What is the fundamental character of one’s political opponents?’. It has values ​​like: Friendly, Mixed, Hostile

1.2. Prospects for Realizing Fundamental Values ​​(P-2): ‘What are the prospects for the eventual realization of one’s fundamental values ​​and aspirations? Can one be optimistic, or must one be pessimistic on this score; and in what respects the one and/or the other?’ It has values ​​like: Optimism versus Pessimism.

1.3. Predictability of the Political Universe (P-3): P-3. ‘Is the political future predictable? In what sense and to what extent?’. It has values ​​like: Low to High.

1.4. Control Over Historical Development (P-4): ‘How much “control” or “mastery” can one have over historical development? What is one’s role in “moving” and “shaping” history in the desired direction?’. It has values ​​like: Low to High.

1.5. Role of Chance (P-5): ‘What is the role of “chance” in human affairs and in historical development?’. It has values ​​like: Low to High.

1. 1. 2. The Instrumental Beliefs in an Operational Code

2.1. Direction of Strategy (I-1): ‘What is the best approach for selecting goals or objectives for political action?’. It has values ​​like: Cooperative Mixed, Conflictual.

2.2. Intensity of Tactics (I-2): ‘How are the goals of action pursued most effectively?’

2.3. Risk Orientation (I-3): ‘How are the risks of political action calculated, controlled, and accepted?’. It has values ​​like: Averse to Acceptant.

2.4. Importance of Timing of Actions (I-4): ‘What is the best “timing” of action to advance one’s interests?’. It has values ​​like: Low Flexibility to High Flexibility.

2.5. Utility of Means (I-5): ‘What is the utility and role of different means for advancing one’s interests?’. It has values ​​like: Low to High.

1. 4. Poliheuristic Predictive Strategic Cyber ​​Intelligence Analysis

The approach to the hierarchical organization of objectives based on the non-compensatory principle of objectives will be explained after two philosophical indicators the nature of the political universe (P-1) and prospects for realizing fundamental values ​​(P-2). For the prospects for realizing fundamental values ​​indicator P-2, two values ​​are defined in the operational code analysis: optimism and pessimism. In the same classic operational code analysis, the nature of the political universe indicator P-1 has three values. In order to obtain ten values ​​corresponding to the ten-weight scale, we will make a combination between the values ​​of the two indicators P-1 and P-2 as in the table (Table 1) below, redefining the values ​​of P-1 according to five steps: High friendly, Low friendly, Mixed, Low hostile, High hostile.

Table 1. Weights related by P-1 and P2

Fig.2. I-2 indicator assigend to escalation and de-escalation in foreign policy (Source: constructed by author)

For a complete analysis in general [22], three indicators are considered sufficient: The Nature of the Political Universe (P-1); Control Over Historical Development (P-4); Direction of Strategy (I-1).

To define the alternatives of the poliheuristic decision matrix, we associate an alternative with the escalation or de-escalation transition from the international COPDAB scale of the direction of strategy indicator I-1 (Fig. 2).

To define the implications, we use the control over historical development indicator (P-4) which deals with the projection of the decision-making actor’s power on the environment in which it exists and the way in which it controls it. If the initial definitions of control over historical development (P-4) were based in the classical operational code analysis on the following two queries:

• ’How much “control” or “mastery” can one have over historical development?’

• ’What is one’s role in “moving” and “shaping” history in the desired direction?’

then these must be amended to adapt to the requirements of poliheuristic predictive strategic cyber intelligence analysis so as to include risk elements of prospect theory such as risk appetite, neutral element, risk aversion and sunk costs. In the new approach we will rephrase the two queries as follows for the first: ’How much “control” or “mastery” can the decision makers have over historical development?’ [23]. This includes the concept of decision makers because this is the central research object of the poliheuristic foreign policy paradigm. For the second question associated with the control over historical development indicator P-4, its connection to the prospect theory, respectively the dimensions of the poliheuristic decision matrix, determines the following text: ‘What is the role of decision makers in “moving” and “shaping” history in the desired direction of domestic dimensions (geopolitical/ domestic politics / diplomacy/ economics/ military/ civil society, etc) related by sunk cost’.

2.4.1. Sunk cost correlated with Dimension outcome

To define the three risk zones specific to the prospect theory associated with the value curve from the same theory, we will use the risk variation indicator defined in the operational code analysis the risk orientation variation [24]. To study the three risk regions, we correlate the value curve graph (Fig. 1) with the resilience curve in (Fig. 3) [25]. We associate resilience with the control over historical development indicator P-4. Thus, the variation of the control over historical development indicator P-4 from pre-event to post-event overlaps the four stages of its resilience (P-4 preparation following a decline from pre-event to post-event, absorption of the P-4 decline from pre-event to post-event, recovery from the P-4 decline from pre-event to post-event, adaptation of P-4 after post-event). Thus, the connection between the value curve and the resilience curve will be achieved as follows:

• In the case where the post-event control over historical development indicator P-4 cannot absorb its fall, then the decision-maker is in the third quadrant of the risk appetite value curve where the control over historical development indicator P-4 cannot recover due to the maintenance or increase of sunk costs.

• In the case where the post-event control over historical development indicator P-4 can absorb its fall and recovery and adaptation bring it to a post-event value comparable to the pre-event one, we find ourselves in the case of a neutral element of the value curve.

• In the case where the post-event control over historical development indicator P-4 can absorb its fall and recovery and adaptation bring it to a post-event value greater than the pre-event one. We find ourselves in the case of risk aversion of the value curve.

For the poliheuristic paradigm, each implication has an associated rate from -10 to +10. For a dimension considered critical, the values ​​are from +1 to +10. The value 0 means that the respective rate is non-important in relation to the critical ones. In this research, the concept of rate is defined as the part of the statement of the indicator P-4 ‘What is one’s role of decision makers in “moving” and “shaping” history in the desired direction of domestic dimensions (geopolitical/ domestic politics / diplomacy/ economics/ military/ civil society, etc) related by sunk cost’. The values ​​of these critical rates are presented in the table below in which the internal political system can create major problems in the foreign policy process of a state and the projection of its power through internal conflicts that are sunk costs for the state actor. The weights 10 specific to High Risk Aversion (in which the government effectively takes internal measures that increase the internal resilience of the state actor) and 9 typical for Low Risk Aversion (in which the government makes declarations of internal measures that increase the internal resilience of the state actor) represent the risk aversion positions in which, following some slippage of resources usable in foreign policy, they manifest resilience and can recover and adapt to the challenges that the state actor must face (Table 2). For the neutral element, the decision-maker adopts “routine, purposive actions” measures that favor absorption, recovery and adaptation avoiding sunk costs (Table 2). For the rates from 1 to 7 that we consider correlated with sunk costs and belong to the area in the third quadrant of risk acceptance are presented in Table 2, following which the state actor cannot absorb its sunk costs.

Fig.3 Resilience curve of system related to prospect theory curve (Source: constructed by author)

Table 2. Relation risk and desired direction of decision maker

(Source: constructed by author)

2.4.2. Simple Majority Voting Method

The second stage of the poliheuristic paradigm establishes decision relationships between weights and rates to establish the hierarchy of alternatives-dimensions, i.e. policy-objective pairs. These will be exemplified in the application below which briefly discusses the fake news attacks organized by the American secret services to overthrow the Arab governments in Egypt, Tunisia, Syria and Libya respectively. The attacks were triggered by cyber attacks through fake news that produced great waves of popular emotion in the four Arab countries that determined changes of governments and social destabilization. First, we will have to evaluate using the Simple Majority Voting Method from the intelligence analysis the weights of the objectives (Egypt, Tunisia, Syria and Libya) respectively the rates of each action specific to an objective. Thus, we use a group of seven voting experts as follows: V.F., T.G., A.H., R.B., M.P., A.J. and R.C. In the Simple Majority Voting Method, the majority of votes are established for a weight from 1-10 specific to a given objective. For Egypt, where the situation was stabilized by the army, we assign a weight of 3 (Table 3) to a state of collective pessimism and an internal conflict with a confused state of cooperation and conflict between all political actors. In Tunisia, although the internal political situation was finally stabilized, it generally had a low-level conflict and general pessimism with a weight of 2 (Table 2). For Syria and Libya, internal conflict and the state of pessimism were at high levels and the weight of the dimension will be in this case 1 (Table 5). For Egypt, for which the army was a stabilizing factor, the successful political resilience of Egyptian society generated a risk aversion rate of 10 (Table 6). In Tunisia, the long institutional experience in this country determined a transition of political resilience towards the preservation of political institutions specific to the neutral element on the value curve with a specific rate of 8 (Table 7). The adverse experiences of Syria and Libya led to generalized internal wars with a specific rate of 1 (Table 8) typical for maximum risk appetite.

Table 3. Simple Majority Voting Method for Weight of Egypt

Table 4. Simple Majority Voting Method for Weight of Tunisia

Table 5. Simple Majority Voting Method for Weight of Syria/Lybia

Table 6. Simple Majority Voting Method for

Rate of Escalade to COPDAB11 in Egypt

Table 7. Simple Majority Voting Method for

Rate of Escalade to COPDAB11 in Tunisia

Table 8. Simple Majority Voting Method for

Rate of Escalade to COPDAB14/15 in Syria/ Lybia

Table 9. Poliheuristic Decision Matrix of an Arab Government

(Source: constructed by author)

In the poliheuristic decision matrix (Table 9) the final decision options of an Arab government are calculated according to the relation (1) in which: Egypt represents the risk aversion case with the final choice of 30, Tunisia the neutral element case with the final choice of 16 and Syria and Libya the risk appetite case with the final choice of 1.

Conclusion

In the current research, we connected the risk orientation variation to sunk costs specific to prospect theory. In the previous research, four indicators of variation of the operational code were used to define risk according to Predictability of the Political Universe (P-3), Role of Chance (P-5), Importance of Timing of Actions (I-4) and Control Over Historical Development (P-4), in the current study everything was solved by synthesizing the connection between P-4 together with the prospect curve and the resilience curve and sunk costs. This article was inaugurated with a presentation of the poliheuristic paradigm of foreign policy, followed by introductions to operational code analysis, prospect theory along with sunk costs and the resilience curve. The final evaluation of the weights and rates specific to the poliheuristic decision matrix was carried out with the help of a group of seven expert voters using the Simple Majority Voting Method from intelligence analysis. Thus, we constructed a decision matrix of an Arab government that explains the risk modes specific to prospect theory. The research demonstrated a unified approach to strategic cyber intelligence analysis based on poliheuristic foreign policy analysis, operational code analysis, and prospect theory. In future research, we aim to increase the consistency of the correlation between sunk costs and the operational code indicators Predictability of the Political Universe (P-3), Role of Chance (P-5), Importance of Timing of Actions (I-4).

Notes

[1] Pherson  R. H., Heuer R. J., (2020),  Structured Analytic Techniques for Intelligence Analysis,  3^rd edition, CQ Press

[2] Mintz, A., Adamsky,D.D., (2019), How Do Leaders Make Decisions?: Evidence from the East and West, Part A (Contributions to Conflict Management, Peace Economics and Development), Emerald Publishing Limited

[3] McDermott, R., (2001), Risk-Taking in International Politics: Prospect Theory in American Foreign Policy, University of Michigan Press

[4] Manjikian, M., (2020), Introduction to Cyber Politics and Policy, CQ Press 

[5] Ben Buchanan, B., (2022), The Hacker and the State: Cyber Attacks and the New Normal of Geopolitics, Harvard University Press

[6] Manjikian, M., (2020), Introduction to Cyber Politics and Policy, CQ Press 

[7] Schafer, M. , Walker, S., (2006), Beliefs and Leadership in World Politics: Methods and Applications of Operational Code Analysis (Advances in Foreign Policy Analysis, Palgrave Macmillan

[8] McDermott, R., (2001), Risk-Taking in International Politics: Prospect Theory in American Foreign Policy, University of Michigan Press

[9] Cartalucci, T., Bowie, N., (2012), Subverting Syria: How CIA Contra Gangs and NGO’s Manufacture, Mislabel and Market Mass Murder, Progressive Press 

[10] Moga, H.,(2023), Actions of Russian Federation on the EU and consequences of Russian belief system, GeoPolitica Revista de Geografie Politica, GeoPolitica si GeoStrategie, XXI, No. 98, pp. 102-108

[11] Manjikian, M., (2020), Introduction to Cyber Politics and Policy, CQ Press 

[12] Ben Buchanan, B., (2022), The Hacker and the State: Cyber Attacks and the New Normal of Geopolitics, Harvard University Press

[13] Moga, H.,(2023), Actions of Russian Federation on the EU and consequences of Russian belief system, GeoPolitica Revista de Geografie Politica, GeoPolitica si GeoStrategie, XXI, No. 98, pp. 102-108

[14] Brantly, A. F., (2018), The Decision to Attack: Military and Intelligence Cyber Decision-Making (Studies in Security and International Affairs), University of Georgia Press 

[15] Mintz, A., (2010), Understanding Foreign Policy Decision Makin, Cambridge University Press

[16] Mintz, A., (2010), Understanding Foreign Policy Decision Makin, Cambridge University Press

[17] Kahneman, D., (2013), Thinking, Fast and Slow, Farrar, Straus and Giroux

[18] McDermott, R., (2001), Risk-Taking in International Politics: Prospect Theory in American Foreign Policy, University of Michigan Press

[19] Mintz, A., (2010), Understanding Foreign Policy Decision Makin, Cambridge University Press

[20] Schafer, M. , Walker, S., (2006), Beliefs and Leadership in World Politics: Methods and Applications of Operational Code Analysis (Advances in Foreign Policy Analysis, Palgrave Macmillan

[21] Schafer, M. , Walker, S., (2006), Beliefs and Leadership in World Politics: Methods and Applications of Operational Code Analysis (Advances in Foreign Policy Analysis, Palgrave Macmillan

[22] Schafer, M. , Walker, S., (2006), Beliefs and Leadership in World Politics: Methods and Applications of Operational Code Analysis (Advances in Foreign Policy Analysis, Palgrave Macmillan

[23] ] Schafer, M. , Walker, S., (2006), Beliefs and Leadership in World Politics: Methods and Applications of Operational Code Analysis (Advances in Foreign Policy Analysis, Palgrave Macmillan

[24] McDermott, R., (2001), Risk-Taking in International Politics: Prospect Theory in American Foreign Policy, University of Michigan Press

[25] Alexander Kott, A., Linkov., I., (2019),  Cyber Resilience of Systems and Networks (Risk, Systems and Decisions) , Springer