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As part of the Paris Agreement, countries are mandated to update and enhance their Nationally Determined Contributions (NDCs) every five years — increasing their ambition with every cycle. The first round of NDCs were submitted starting in 2015 as intended nationally determined contributions and countries have submitted or are finalizing their updates for the new cycle in 2020/2021. The purpose of this project was to develop a methodology to systematically assess the design of NDCs and to apply it to 20 NDCs. While there are several tools that analyse NDCs, they are comprehensive, concise, replicable and comparable to a varying degree. Our methodology aims to fill a gap by addressing those criteria and providing new insights into the design of NDCs. We evaluate the NDCs along key elements that are critical for the success of the NDC ambition cycle: 1. Increased mitigation ambition; 2. Comprehensiveness; 3. Implementation plan; and 4. Transparency. We have applied this methodology to 20 NDCs with the aim to include a diversity of geographic contexts as well as NDCs with different emphasis, target types, and approaches to climate change mitigation. The analysis shows that the selected countries fare generally well on comprehensiveness and transparency in their new NDCs. The rules under the Paris Agreement on what to include in the NDC and how to describe it show a positive effect; most countries provide the information necessary according to the rules. However, the elements that are critical for the success of the ambition cycle, i.e. raising ambition in each round and having a plan to implement the targets, are only partially covered by many countries. Of the analysed NDCs, less than half showed higher mitigation ambition compared to their first NDC. Many targets remained unchanged or were found to be less ambitious due to changes in parameters such as baseline emissions. Many countries lack a clear implementation plan for the targets: In many cases measures have not yet been included in national legislation, but preparations are underway especially for those countries that increased their targets. Currently, governments can make their NDCs "look good" by fulfilling all requirements on comprehensiveness and transparency, but only a deeper analysis reveals that their NDCs are not more ambitious or lack an implementation plan. To uncover such deficiencies and to make the ambition cycle of the Paris Agreement a success, a careful checking of NDCs is necessary.

The criminal complicity classification is still an issue. Despite the large number of studies on the topic, some issues remain unresolved. In the study, the complicity institution was analyzed in accordance with the systemic approach, which allowed for the substantiation of existing controversies as well as the proposal of solutions.

Biographical analysis has been a traditional device by which historians and social scientists have sought to relate changes in social structure to political developments. All too often, the richness of personal detail has been hung on a "great man" theory of history, without regard to the key leadership groups involved.

In statistical privacy, a privacy definition is regarded as a set of algorithms that are allowed to process sensitive data. It is often helpful to consider the complementary view that privacy definitions are also contracts that guide the behavior of algorithms that take in sensitive data and produce sanitized data. Historically, data privacy breaches have been the result of fundamental misunderstandings about what a particular privacy definition guarantees.
Privacy definitions are often analyzed using a highly targeted approach: a specific attack strategy is evaluated to determine if a specific type of information can be inferred. If the attack works, one can conclude that the privacy definition is too weak. If it doesn't work, one often gains little information about its security (perhaps a slightly different attack would have worked?). Furthermore, these strategies will not identify cases where a privacy definition protects unnecessary pieces of information.
On the other hand, technical results concerning generalizable and systematic analyses of privacy are few in number, but such results have significantly advanced our understanding of the design of privacy definitions. We add to this literature with a novel methodology for analyzing the Bayesian properties of a privacy definition. Its goal is to identify precisely the type of information being protected, hence making it easier to identify (and later remove) unnecessary data protections.
Using privacy building blocks (which we refer to as axioms), we turn questions about semantics into mathematical problems -- the construction of a consistent normal form and the subsequent construction of the row cone (which is a geometric object that encapsulates Bayesian guarantees provided by a privacy definition).
We apply these ideas to study randomized response, FRAPP/PRAM, and several algorithms that add integer-valued noise to their inputs; we show that their privacy properties can be stated in terms of the protection of various notions of parity of a dataset. Randomized response, in particular, provides unnecessarily strong protections for parity, and so we also show how our methodology can be used to relax privacy definitions.