Complexity of Bribery and Control for Uniform Premise-Based Quota Rules Under Various Preference Types

International audience

Judgment aggregation is a collective decision making framework where the opinions of a group of agents is combined into a collective opinion. This can be done using many different judgment aggregation procedures. We study the computational complexity of computing the group opinion for several of the most prominent judgment aggregation procedures. In particular, we show that the complexity of this winner determination problem for analogues of the Kemeny rule, the Slater rule and the Young rule lies at the Θp2-level of the Polynomial Hierarchy (PH). Moreover, we show that the problem has a complexity at the Δp2-level of the PH for the analogue of Tideman's procedure with a fixed tie-breaking rule, and at the Σp2-level of the PH for the analogue of Tideman's procedure without a fixed tie-breaking rule.

ABSTRACTThe Ostrogorski paradox and the discursive dilemma are seemingly unrelated paradoxes of aggregation. The former is discussed in traditional social choice theory, while the latter is at the core of the new literature on judgment aggregation. Both paradoxes arise when, in a group, each individual consistently makes a judgment, or expresses a preference, (in the form of yes or no) over specific propositions, and the collective outcome is in some respect inconsistent. While the result is logically inconsistent in the case of the discursive paradox, it is not stable with respect to the level of aggregation in the case of the Ostrogorski paradox. In the following I argue that, despite these differences, the two problems have a similar structure. My conclusion will be twofold: on the one hand, the similarities between the paradoxes support the claim that these problems should be tackled using the same aggregation procedure; on the other hand, applying the same procedure to these paradoxes will help clarify the strengths and weaknesses of the aggregation method itself. More specifically, I will show that an operator defined in artificial intelligence to merge belief bases can deal with both paradoxes.

Responsive Regulation advocates a differentiated style of regulation and enforcement that is more responsive to the behavior of the regulated parties than a system of general or uniform rules. This article investigates whether such a differentiated approach can be reconciled with the traditional ideal of general law. On the basis of a conceptual analysis of generality, it is argued that the notion of generality is at best tautological and not inconsistent with a differentiated approach to regulation and enforcement. However, the value of generality is based on the assumption that rules function as reasons, rather than as instructions. As reasons, rules need not comprise large categories, but they do need to last for a long period of time. The conclusion drawn is that flexibility (rather than differentiation) is hard to reconcile with the notion of rules as reasons, although it may be demanded by a notion of rules as effective implementers of policies.

Judgment aggregation and agenda manipulation

Drawing on dual-process theory, we suggest that the benefits that arise from combining several quantitative individual judgments will be heightened when these judgments are based on different cognitive processes. We tested this hypothesis in three experimental studies in which participants provided estimates for the dates of different historical events (Study 1, N = 152), made probabilistic forecasts for the outcomes of soccer games (Study 2, N = 98), and estimated the weight of individuals on the basis of a photograph (Study 3, N = 3,695). For each of these tasks, participants were prompted to make judgments relying on an analytical process, on their intuition, or (in a control condition) on no specific instructions. Across all three studies, our results show that an aggregation of intuitive and analytical judgments provides more accurate estimates than any other aggregation procedure and that this advantage increases with the number of aggregated judgments.

Drawing on dual process theory, we suggest that the benefits that arise from combining different individual judgments will be heightened when these judgments are based on different cognitive processes. We test this hypothesis in two experimental studies in which participants were prompted to make judgments relying on an analytical process, on their intuition, or in a control condition in which no particular instructions were given. Our results show that an aggregation of intuitive and analytical judgments provides more accurate estimates than any other aggregation procedure, and that this advantage increases with the number of judgments that are aggregated. Moreover, we find evidence that this result is driven by a lower correlation between errors of intuitive and analytical judgments compared to errors of judgments that are based on the same cognitive process or judgment errors in the control condition.

In this paper, we propose solid semantics to obtain the most acceptable sets of arguments. Besides, we study the application of solid semantics in the field of judgement aggregation.

This special issue of Social Choice and Welfare collects papers that study the interplay between deliberative and aggregative approaches to the formation of collective attitudes. Most of the contributions in this issue focus on so-called aggregative collective attitudes and, in particular, on aggregated beliefs, judgments, and preferences. As their name suggests, this type of collective attitude is defined through the mechanism that leads to its formation: aggregative collective attitudes are those that are obtained by putting together, i.e. aggregating, the attitudes of individuals. Beliefs, judgments, or preferences obtained through some form of voting are paradigm examples of aggregative attitudes. The properties of aggregative collective attitudes and their corresponding aggregation procedures are well studied. Social Choice Theory (Gaertner 2009) has mapped the possibilities and also the limits of the aggregation of preferences. The theory of Judgment Aggregation (Grossi and Pigozzi 2014) has done the same for the aggregation of categorical, logically connected judgments. Aggregative attitudes can be distinguished from common and from corporate ones (List 2014). Common collective attitudes are those that all group members share, and perhaps that are even public in the relevant sense (Williams 2023). Notions like common knowledge and common beliefs, central to theories of collective agency (Chant 2008) and epistemic game theory (Pacuit and Roy 2017), for instance, are probably the most well-known examples of common group attitudes. Corporate collective attitudes, on the other hand, are those that can be attributed to institutional or corporate agents based on their behavior or status (Tollefsen 2002; List and Pettit 2011) . The main difference between these three types of group attitudes is how they relate to the attitudes of the individual members. Common attitudes have the most direct relation: by definition, they are consensual and public. If a proposition is commonly believed, for instance, then every group member believes that proposition, believes that every group member believes it, and so on. This is not necessarily the case for aggregative or corporate attitudes. Voting, for instance, is typically viewed as a mechanism to aggregate diverging opinions. It is not meant to reflect an underlying consensus B Olivier Roy

Next point-of-interest (POI) recommendation has emerged as a key research topic within location-based social networks. Prior studies suggest that combining users’ enduring preferences with their short times behaviors can substantially improve recommendation quality. However, since manually annotated labels distinguishing these two forms of interest are usually unavailable, many existing models tend to merge them during training, resulting in less accurate representations. In addition, the interaction between stable and dynamic interests is complex, and current approaches often struggle to model the evolving dependency. To overcome these challenges, we introduce a novel recommendation framework. Specifically, we adopt two bidirectional Transformer encoders to separately model general tastes and dynamic preferences. Preference labels are derived from users’ historical trajectories, and contrastive learning is applied to realize self-supervised separation of long- and short-term interests. Moreover, an attention mechanism with adaptive weighting is designed to integrate these two types of preferences to capture their dynamically changing dependencies. By leveraging spatio-temporal correlations across short-term and long-term sequences, our method generates more robust and distinctive preference representations. According to our review of prior studies, this work is the first POI recommendation study to explicitly distinguish between long- and short-term user preferences. Experiments conducted across three real-world datasets validate the performance of our method, showing average improvements of 23.51% (HR@5), 20.45% (HR@10), 14.18% (NDCG@5), and 17.16% (NDCG@10) over strong baselines.

Traveling to a place with a period of days requires a place to stay. The problem is determining accommodation according to the wishes of different visitors, such as prices according to the type of lodging, complete facilities or not at the inn, and the distance to other places from the inn. There are too many lodging options that make it difficult for visitors to determine the best alternative. This research focuses on lodging in the Malioboro area of Yogyakarta by using the criteria of distance, price, class of accommodation, room facilities, and supporting facilities. AHP method is used to determine the weight of each criterion, then the weight of each criterion is calculated using Promethee with the usual or general criteria preference type, to produce a net flow value. Combination of two methods is better because the AHP method is more suitable to be used to find the weight of each criterion and Promethee by using a preference index as needed by producing suitable alternatives. The result of AHP and Promethee calculations obtained with the PP3 alternativewith a net flow value of 0.2 to be the first rank and it can be stated that the existing criteria are very suitable for what visitors want.